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Gold-standard diagnosis of Alzheimer’s disease (AD) relies on histopathological staging systems. Using the topographical information from [ 18 F]MK6240 tau positron-emission tomography (PET), we applied the Braak tau staging system to 324 living individuals. We used PET-based Braak stage to model the trajectories of amyloid-β, phosphorylated tau (pTau) in cerebrospinal fluid (pTau 181 , pTau 217 , pTau 231 and pTau 235 ) and plasma (pTau 181 and pTau 231 ), neurodegeneration and cognitive symptoms. We identified nonlinear AD biomarker trajectories corresponding to the spatial extent of tau-PET, with modest biomarker changes detectable by Braak stage II and significant changes occurring at stages III–IV, followed by plateaus. Early Braak stages were associated with isolated memory impairment, whereas Braak stages V–VI were incompatible with normal cognition. In 159 individuals with follow-up tau-PET, progression beyond stage III took place uniquely in the presence of amyloid-β positivity. Our findings support PET-based Braak staging as a framework to model the natural history of AD and monitor AD severity in living humans.

Blood-based biomarkers for screening may guide tau positrion emissition tomography (PET) scan referrals to optimize prognostic evaluation in Alzheimer’s disease. Plasma Aβ42/Aβ40, pTau181, pTau217, pTau231, NfL, and GFAP were measured along with tau-PET in memory clinic patients with subjective cognitive decline, mild cognitive impairment or dementia, in the Swedish BioFINDER-2 study (n = 548) and in the TRIAD study (n = 179). For each plasma biomarker, cutoffs were determined for 90%, 95%, or 97.5% sensitivity to detect tau-PET-positivity. We calculated the percentage of patients below the cutoffs (who would not undergo tau-PET; “saved scans”) and the tau-PET-positivity rate among participants above the cutoffs (who would undergo tau-PET; “positive predictive value”). Generally, plasma pTau217 performed best. At the 95% sensitivity cutoff in both cohorts, pTau217 resulted in avoiding nearly half tau-PET scans, with a tau-PET-positivity rate among those who would be referred for a scan around 70%. And although tau-PET was strongly associated with subsequent cognitive decline, in BioFINDER-2 it predicted cognitive decline only among individuals above the referral cutoff on plasma pTau217, supporting that this workflow could reduce prognostically uninformative tau-PET scans. In conclusion, plasma pTau217 may guide selection of patients for tau-PET, when accurate prognostic information is of clinical value. A screening strategy with plasma p-tau217, evaluated in two independent cohorts from Sweden and Canada, showed that this biomarker may effectively streamline tau-PET referrals in memory clinic settings, optimizing the prognostic work-up of Alzheimer’s disease.

Obsessive-compulsive disorder (OCD) is a chronic, incapacitating, early onset psychiatric disorder that is characterized by obsessions and compulsions originating from a disturbance in the cortico-striato-thalamico-cortical circuit. We implemented the preclinical quinpirole (QP) rat model for compulsive checking in OCD to analyse the behaviour and visualize the D2R, mGluR5 and GLT1 density in order to contribute to the understanding of the neuroreceptor kinetics. Animals (n = 14) were exposed to either saline (1 mL/kg) or QP (dopamine D2-agonist, 0.5 mg/kg) twice-weekly during 7 weeks. After each injection animals were placed on an open field test. After model setup, animals were placed in a behavioural cage equipped with tracking software and hardware in order to analyse the behaviour. Subsequently, sagittal slides were made of the CP in the right hemisphere and a staining was done with the D2R, mGluR5 and GLT-1 antibody to visualize the corresponding receptor. The QP animals displayed a strong increase in travelled distance (+596.70%) and in the number of homebase visits (+1222.90%) compared to the control animals. After chronic exposure to QP, animals had a significantly (p < 0.05) higher percentage of D2R density in the CP (7.92% ± 0.48%) versus 6.66% ± 0.28% in animals treated with saline. There were no differences for mGluR5 and GLT1 receptor density. Chronic exposure to QP leads to hyperlocomotion and an increase in D2R density. Furthermore, as mGluR5 and GLT1 density did not seem to be directly affected, decreased levels of glutamate might have influenced the binding potential in earlier reports.

Previous studies suggest glial and neuronal changes may trigger synaptic dysfunction in Alzheimer’s disease (AD), but the link between their markers and synaptic abnormalities in the living brain remains unclear. We investigated the association between glial reactivity and synaptic dysfunction biomarkers in cerebrospinal fluid (CSF) from 478 individuals in cognitively unimpaired (CU) and cognitively impaired (CI) individuals. We measured amyloid-β (Aβ), phosphorylated tau (pTau181), astrocyte reactivity (GFAP), microglial activation (sTREM2), and synaptic markers (GAP43, neurogranin). CSF GFAP levels were associated with presynaptic and postsynaptic dysfunction, independent of cognitive status or Aβ presence. CSF sTREM2 levels were related to presynaptic markers in cognitively unimpaired and impaired Aβ+ individuals, and to postsynaptic markers in cognitively impaired Aβ+ individuals. Notably, CSF pTau mediated the relationships between GFAP or sTREM2 and synaptic dysfunction. Our findings, validated in two independent cohorts (TRIAD and ADNI), reveal a distinct pattern of glial contribution to synaptic degeneration. This study shows that glial reactivity is linked to synaptic dysfunction in aging and Alzheimer’s disease, with tau pathology mediating these effects–highlighting the potential of synaptic biomarkers track disease progression.

Recently approved anti-amyloid immunotherapies for Alzheimer’s disease (AD) require evidence of amyloid-β pathology from positron emission tomography (PET) or cerebrospinal fluid (CSF) before initiating treatment. Blood-based biomarkers promise to reduce the need for PET or CSF testing; however, their interpretation at the individual level and the circumstances requiring confirmatory testing are poorly understood. Individual-level interpretation of diagnostic test results requires knowledge of disease prevalence in relation to clinical presentation (clinical pretest probability). Here, in a study of 6,896 individuals evaluated from 11 cohort studies from six countries, we determined the positive and negative predictive value of five plasma biomarkers for amyloid-β pathology in cognitively impaired individuals in relation to clinical pretest probability. We observed that p-tau217 could rule in amyloid-β pathology in individuals with probable AD dementia (positive predictive value above 95%). In mild cognitive impairment, p-tau217 interpretation depended on patient age. Negative p-tau217 results could rule out amyloid-β pathology in individuals with non-AD dementia syndromes (negative predictive value between 90% and 99%). Our findings provide a framework for the individual-level interpretation of plasma biomarkers, suggesting that p-tau217 combined with clinical phenotyping can identify patients where amyloid-β pathology can be ruled in or out without the need for PET or CSF confirmatory testing. Therriault et al. provide a framework for the individual-level interpretation of plasma biomarkers by determining their positive and negative predictive values for amyloid positron emission tomography status in relation to patient age and clinical symptoms.

Purpose This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC). Methods We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with ‘n_splits’ set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome. Results From 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity. Conclusion Radiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.

Purpose This study aimed to select robust features against lung motion in a phantom study and use them as input to feature selection algorithms and machine learning classifiers in a clinical study to predict the lymphovascular invasion (LVI) of non-small cell lung cancer (NSCLC). The results of robust features were also compared with conventional techniques without considering the robustness of radiomic features. Methods An in-house developed lung phantom was developed with two 22mm lesion sizes based on a clinical study. A specific motor was built to simulate motion in two orthogonal directions. Lesions of both clinical and phantom studies were segmented using a Fuzzy C-means-based segmentation algorithm. After inducing motion and extracting 105 radiomic features in 4 feature sets, including shape, first-, second-, and higher-order statistics features from each region of interest (ROI) of the phantom image, statistical analyses were performed to select robust features against motion. Subsequently, these robust features and a total of 105 radiomic features were extracted from 126 clinical data. Various feature selection (FS) and multiple machine learning (ML) classifiers were implemented to predict the LVI of NSCLC, followed by comparing the results of predicting LVI using robust features with common conventional techniques not considering the robustness of radiomic features. Results Our results demonstrated that selecting robust features as input to FS algorithms and ML classifiers surges the sensitivity, which has a gentle negative effect on the accuracy and the area under the curve (AUC) of predictions compared with commonly used methods in 12 of 15 outcomes. The top performance of the LVI prediction was achieved by the NB classifier and RFE FS without considering the robustness of radiomic features with 95% area under the curve of AUC, 67% accuracy, and 100% sensitivity. Moreover, the top performance of the LVI prediction using robust features belonged to the NB classifier and Boruta feature selection with 92% AUC, 86% accuracy, and 100% sensitivity. Conclusion Robustness over various influential factors is critical and should be considered in a radiomic study. Selecting robust features is a solution to overcome the low reproducibility of radiomic features. Although setting robust features against motion in a phantom study has a minor negative impact on the accuracy and AUC of LVI prediction, it boosts the sensitivity of prediction to a large extent.

Background Alzheimer’s disease is characterized by an abnormal increase of phosphorylated tau (pTau) species in the CSF. It has been suggested that emergence of different pTau forms may parallel disease progression. Therefore, targeting multiple specific pTau forms may allow for a deeper understanding of disease evolution and underlying pathophysiology. Current immunoassays measure pTau epitopes separately and may capture phosphorylated tau fragments of different length depending on the non-pTau antibody used in the assay sandwich pair, which bias the measurement. Methods We developed the first antibody-free mass spectrometric method to simultaneously measure multiple phosphorylated epitopes in CSF tau: pT181, pS199, pS202, pT205, pT217, pT231, and pS396. The method was first evaluated in biochemically defined Alzheimer’s disease and control CSF samples ( n  = 38). All seven pTau epitopes clearly separated Alzheimer’s disease from non-AD ( p  < 0.001, AUC = 0.84–0.98). We proceeded with clinical validation of the method in the TRIAD ( n  = 165) and BioFINDER-2 cohorts ( n  = 563), consisting of patients across the full Alzheimer’s disease continuum , including also young controls (< 40 years), as well as patients with frontotemporal dementia and other neurodegenerative disorders. Results Increased levels of all phosphorylated epitopes were found in Alzheimer’s disease dementia and Aβ positron emission tomography-positive patients with mild cognitive impairment compared with Aβ-negative controls. For Alzheimer’s disease dementia compared with Aβ-negative controls, the best biomarker performance was observed for pT231 (TRIAD: AUC = 98.73%, fold change = 7.64; BioFINDER-2: AUC = 91.89%, fold change = 10.65), pT217 (TRIAD: AUC = 99.71%, fold change = 6.33; BioFINDER-2: AUC = 98.12%, fold change = 8.83) and pT205 (TRIAD: AUC = 99.07%, fold change = 5.34; BioFINDER-2: AUC = 93.51%, fold change = 3.92). These phospho-epitopes also discriminated between Aβ-positive and Aβ-negative cognitively unimpaired individuals: pT217 (TRIAD: AUC = 83.26, fold change = 2.39; BioFINDER-2: AUC = 91.05%, fold change = 3.29), pT231 (TRIAD: AUC = 86.25, fold change = 3.80; BioFINDER-2: AUC = 78.69%, fold change = 3.65) and pT205 (TRIAD: AUC = 71.58, fold change = 1.51; BioFINDER-2: AUC = 71.11%, fold change = 1.70). Conclusions While an increase was found for all pTau species examined, the highest fold change in Alzheimer’s disease was found for pT231, pT217 and pT205. Simultaneous antibody-free measurement of pTau epitopes by mass spectrometry avoids possible bias caused by differences in antibody affinity for modified or processed forms of tau, provides insights into tau pathophysiology and may facilitate clinical trials on tau-based drug candidates.

Recent studies suggest a framework where white-matter (WM) atrophy plays an important role in fronto-temporal dementia (FTD) pathophysiology. However, these studies often overlook the fact that WM tracts bridging different brain regions may have different vulnerabilities to the disease and the relative contribution of grey-matter (GM) atrophy to this WM model, resulting in a less comprehensive understanding of the relationship between clinical symptoms and pathology. Using a common factor analysis to extract a semantic and an executive factor, we aimed to test the relative contribution of WM and GM of specific tracts in predicting cognition in the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI). We found that semantic symptoms were mainly dependent on short-range WM fibre disruption, while damage to long-range WM fibres was preferentially associated to executive dysfunction with the GM contribution to cognition being predominant for local processing. These results support the importance of the disruption of specific WM tracts to the core cognitive symptoms associated with FTD. As large-scale WM tracts, which are particularly vulnerable to vascular disease, were highly associated with executive dysfunction, our findings highlight the importance of controlling for risk factors associated with deep WM disease, such as vascular risk factors, in patients with FTD in order not to potentiate underlying executive dysfunction.

Background Impaired kidney function has a potential confounding effect on blood biomarker levels, including biomarkers for Alzheimer’s disease (AD). Given the imminent use of certain blood biomarkers in the routine diagnostic work-up of patients with suspected AD, knowledge on the potential impact of comorbidities on the utility of blood biomarkers is important. We aimed to evaluate the association between kidney function, assessed through estimated glomerular filtration rate (eGFR) calculated from plasma creatinine and AD blood biomarkers, as well as their influence over predicting Aβ-positivity. Methods We included 242 participants from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort, comprising cognitively unimpaired individuals (CU; n  = 124), mild cognitive impairment (MCI; n  = 58), AD dementia ( n  = 34), and non-AD dementia ( n  = 26) patients all characterized by [ 18 F] AZD-4694. Plasma samples were analyzed for Aβ42, Aβ40, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), tau phosphorylated at threonine 181 (p-tau181), 217 ( p -tau217), 231 (p-tau231) and N-terminal containing tau fragments (NTA-tau) using Simoa technology. Kidney function was assessed by eGFR in mL/min/1.73 m 2 , based on plasma creatinine levels, age, and sex. Participants were also stratified according to their eGFR-indexed stages of chronic kidney disease (CKD). We evaluated the association between eGFR and blood biomarker levels with linear models and assessed whether eGFR provided added predictive value to determine Aβ-positivity with logistic regression models. Results Biomarker concentrations were highest in individuals with CKD stage 3, followed by stages 2 and 1, but differences were only significant for NfL, Aβ42, and Aβ40 (not Aβ42/Aβ40). All investigated biomarkers showed significant associations with eGFR except plasma NTA-tau, with stronger relationships observed for Aβ40 and NfL. However, after adjusting for either age, sex or Aβ-PET SUVr, the association with eGFR was no longer significant for all biomarkers except Aβ40, Aβ42, NfL, and GFAP. When evaluating whether accounting for kidney function could lead to improved prediction of Aβ-positivity, we observed no improvements in model fit (Akaike Information Criterion, AIC) or in discriminative performance (AUC) by adding eGFR to a base model including each plasma biomarker, age, and sex. While covariates like age and sex improved model fit, eGFR contributed minimally, and there were no significant differences in clinical discrimination based on AUC values. Conclusions We found that kidney function seems to be associated with AD blood biomarker concentrations. However, these associations did not remain significant after adjusting for age and sex, except for Aβ40, Aβ42, NfL, and GFAP. While covariates such as age and sex improved prediction of Aβ-positivity, including eGFR in the models did not lead to improved prediction for any biomarker. Our findings indicate that renal function, within the normal to mild impairment range, does not seem to have a clinically relevant impact when using highly accurate blood biomarkers, such as p-tau217, in a biomarker-supported diagnosis.