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Alzheimers disease typically progresses in stages, which have been defined by the presence of disease-specific biomarkers: amyloid (A), tau (T) and neurodegeneration (N). This progression of biomarkers has been condensed into the ATN framework, in which each of the biomarkers can be either positive (+) or negative (-). Over the past decades, genome-wide association studies have implicated ∼90 different loci involved with the development of late-onset Alzheimers disease. Here, we investigate whether genetic risk for Alzheimers disease contributes equally to the progression in different disease stages or whether it exhibits a stage-dependent effect. Amyloid (A) and tau (T) status was defined using a combination of available PET and CSF biomarkers in the Alzheimers Disease Neuroimaging Initiative cohort. In 312 participants with biomarker-confirmed A-T- status, we used Cox proportional hazards models to estimate the contribution of APOE and polygenic risk scores (beyond APOE) to convert to A+T- status (65 conversions). Furthermore, we repeated the analysis in 290 participants with A+T- status and investigated the genetic contribution to conversion to A+T+ (45 conversions). Both survival analyses were adjusted for age, sex and years of education. For progression from A-T- to A+T-, APOE-e4 burden showed a significant effect [hazard ratio (HR) = 2.88; 95% confidence interval (CI): 1.70-4.89; P < 0.001], whereas polygenic risk did not (HR = 1.09; 95% CI: 0.84-1.42; P = 0.53). Conversely, for the transition from A+T- to A+T+, the contribution of APOE-e4 burden was reduced (HR = 1.62; 95% CI: 1.05-2.51; P = 0.031), whereas the polygenic risk showed an increased contribution (HR = 1.73; 95% CI: 1.27-2.36; P < 0.001). The marginal APOE effect was driven by e4 homozygotes (HR = 2.58; 95% CI: 1.05-6.35; P = 0.039) as opposed to e4 heterozygotes (HR = 1.74; 95% CI: 0.87-3.49; P = 0.12). The genetic risk for late-onset Alzheimers disease unfolds in a disease stage-dependent fashion. A better understanding of the interplay between disease stage and genetic risk can lead to a more mechanistic understanding of the transition between ATN stages and a better understanding of the molecular processes leading to Alzheimers disease, in addition to opening therapeutic windows for targeted interventions.

Stargardt atrophy and geographic atrophy (GA) represent pivotal endpoints in FDA-approved clinical trials. Predicting atrophy progression is crucial for evaluating drug efficacy. Fundus autofluorescence (FAF), the standard 2D imaging modality in these trials, has limitations in patient comfort. In contrast, spectral-domain optical coherence tomography (SD-OCT), a 3D imaging modality, is more patient friendly but suffers from lower image quality. This study has two primary objectives: (1) develop an efficient predictive modeling for the generation of future FAF images and prediction of future Stargardt atrophic (as well as GA) regions and (2) develop an efficient predictive modeling with advanced 3D OCT features at ellipsoid zone (EZ) for the comparative performance in the generation of future enface EZ maps and prediction of future Stargardt atrophic regions on OCT as on FAF. To achieve these goals, we propose two deep neural networks (termed ReConNet and ReConNet-Ensemble) with recurrent learning units (long short-term memory, LSTM) integrating with a convolutional neural network (CNN) encoder–decoder architecture and concurrent learning units integrated by ensemble/multiple recurrent learning channels. The ReConNet, which incorporates LSTM connections with CNN, is developed for the first goal on longitudinal FAF. The ReConNet-Ensemble, which incorporates multiple recurrent learning channels based on enhanced EZ enface maps to capture higher-order inherent OCT EZ features, is developed for the second goal on longitudinal OCT. Using FAF images at months 0, 6, and 12 to predict atrophy at month 18, the ReConNet achieved mean (±standard deviation, SD) and median Dice coefficients of 0.895 (±0.086) and 0.922 for Stargardt atrophy and 0.864 (±0.113) and 0.893 for GA. Using SD-OCT images at months 0 and 6 to predict atrophy at month 12, the ReConNet-Ensemble achieved mean and median Dice coefficients of 0.882 (±0.101) and 0.906 for Stargardt atrophy. The prediction performance on OCT images is comparably good to that on FAF. These results underscore the potential of SD-OCT for efficient and practical assessment of atrophy progression in clinical trials and retina clinics, complementing or surpassing the widely used FAF imaging technique. [ABSTRACT FROM AUTHOR]

Stargardt atrophy and geographic atrophy (GA) represent pivotal endpoints in FDA-approved clinical trials. Predicting atrophy progression is crucial for evaluating drug efficacy. Fundus autofluorescence (FAF), the standard 2D imaging modality in these trials, has limitations in patient comfort. In contrast, spectral-domain optical coherence tomography (SD-OCT), a 3D imaging modality, is more patient friendly but suffers from lower image quality. This study has two primary objectives: (1) develop an efficient predictive modeling for the generation of future FAF images and prediction of future Stargardt atrophic (as well as GA) regions and (2) develop an efficient predictive modeling with advanced 3D OCT features at ellipsoid zone (EZ) for the comparative performance in the generation of future enface EZ maps and prediction of future Stargardt atrophic regions on OCT as on FAF. To achieve these goals, we propose two deep neural networks (termed ReConNet and ReConNet-Ensemble) with recurrent learning units (long short-term memory, LSTM) integrating with a convolutional neural network (CNN) encoder–decoder architecture and concurrent learning units integrated by ensemble/multiple recurrent learning channels. The ReConNet, which incorporates LSTM connections with CNN, is developed for the first goal on longitudinal FAF. The ReConNet-Ensemble, which incorporates multiple recurrent learning channels based on enhanced EZ enface maps to capture higher-order inherent OCT EZ features, is developed for the second goal on longitudinal OCT. Using FAF images at months 0, 6, and 12 to predict atrophy at month 18, the ReConNet achieved mean (±standard deviation, SD) and median Dice coefficients of 0.895 (±0.086) and 0.922 for Stargardt atrophy and 0.864 (±0.113) and 0.893 for GA. Using SD-OCT images at months 0 and 6 to predict atrophy at month 12, the ReConNet-Ensemble achieved mean and median Dice coefficients of 0.882 (±0.101) and 0.906 for Stargardt atrophy. The prediction performance on OCT images is comparably good to that on FAF. These results underscore the potential of SD-OCT for efficient and practical assessment of atrophy progression in clinical trials and retina clinics, complementing or surpassing the widely used FAF imaging technique.

Summary: Background: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses a worldwide public health challenge. A neuroimaging biomarker would significantly improve early diagnosis and intervention, ultimately enhancing the quality of life for affected individuals and reducing the burden on healthcare systems. Methods: Cross-sectional and longitudinal data (10,099 participants with 13,380 scans) from 12 independent datasets were used in the present study (this study was performed between September 1, 2021 and February 15, 2023). The Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN) score was developed via integrated regional- and network-based measures under an ensemble machine learning model based on structural MRI data. We systematically assessed whether IBRAIN could be a neuroimaging biomarker for AD. Findings: IBRAIN accurately differentiated individuals with AD from NCs (AUC = 0.92) and other neurodegenerative diseases, including Frontotemporal dementia (FTD), Parkinson's disease (PD), Vascular dementia (VaD) and Amyotrophic Lateral Sclerosis (ALS) (AUC = 0.92). IBRAIN was significantly correlated to clinical measures and gene expression, enriched in immune process and protein metabolism. The IBRAIN score exhibited a significant ability to reveal the distinct progression of prodromal AD (i.e., Mild cognitive impairment, MCI) (Hazard Ratio (HR) = 6.52 [95% CI: 4.42∼9.62], p

Objective: The aim of this study was the comprehensive characterisation of longitudinal clinical, electrophysiological and neuroimaging measures in type III and IV adult spinal muscular atrophy (SMA) with a view to propose objective monitoring markers for future clinical trials. Methods: Fourteen type III or IV SMA patients underwent standardised assessments including muscle strength testing, functional evaluation (SMAFRS and MFM), MUNIX (abductor pollicis brevis, APB; abductor digiti minimi, ADM; deltoid; tibialis anterior, TA; trapezius) and quantitative cervical spinal cord MRI to appraise segmental grey and white matter atrophy. Patients underwent a follow-up assessment with the same protocol 24 months later. Longitudinal comparisons were conducted using the Wilcoxon-test for matched data. Responsiveness was estimated using standardized response means (SRM) and a composite score was generated based on the three most significant variables. Results: Significant functional decline was observed based on SMAFRS (p = 0.019), pinch and knee flexion strength (p = 0.030 and 0.027), MUNIX and MUSIX value in the ADM (p = 0.0006 and 0.043) and in TA muscle (p = 0.025). No significant differences were observed based on cervical MRI measures. A significant reduction was detected in the composite score (p = 0.0005, SRM = −1.52), which was the most responsive variable and required a smaller number of patients than single variables in the estimation of sample size for clinical trials. Conclusions: Quantitative strength testing, SMAFRS and MUNIX readily capture disease progression in adult SMA patients. Composite multimodal scores increase predictive value and may reduce sample size requirements in clinical trials. [ABSTRACT FROM AUTHOR]

Positron emission tomography (PET)‐based staging of regional amyloid deposition has recently emerged as a promising tool for sensitive detection and stratification of pathology progression in Alzheimer's Disease (AD). Here we present an updated methodological framework for PET‐based amyloid staging using region–specific amyloid‐positivity thresholds and assess its longitudinal validity using serial PET acquisitions. We defined region‐specific thresholds of amyloid‐positivity based on Florbetapir‐PET data of 13 young healthy individuals (age ≤ 45y), applied these thresholds to Florbetapir‐PET data of 179 cognitively normal older individuals to estimate a regional amyloid staging model, and tested this model in a larger sample of patients with mild cognitive impairment (N = 403) and AD dementia (N = 85). 2‐year follow‐up Florbetapir‐PET scans from a subset of this sample (N = 436) were used to assess the longitudinal validity of the cross‐sectional model based on individual stage transitions and data‐driven longitudinal trajectory modeling. Results show a remarkable congruence between cross‐sectionally estimated and longitudinally modeled trajectories of amyloid accumulation, beginning in anterior temporal areas, followed by frontal and medial parietal areas, the remaining associative neocortex, and finally primary sensory‐motor areas and subcortical regions. Over 98% of individual amyloid deposition profiles and longitudinal stage transitions adhered to this staging scheme of regional pathology progression, which was further supported by corresponding changes in cerebrospinal fluid biomarkers. In conclusion, we provide a methodological refinement and longitudinal validation of PET‐based staging of regional amyloid accumulation, which may help improving early detection and in‐vivo stratification of pathologic disease progression in AD. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses a worldwide public health challenge. A neuroimaging biomarker would significantly improve early diagnosis and intervention, ultimately enhancing the quality of life for affected individuals and reducing the burden on healthcare systems., Methods: Cross-sectional and longitudinal data (10,099 participants with 13,380 scans) from 12 independent datasets were used in the present study (this study was performed between September 1, 2021 and February 15, 2023). The Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN) score was developed via integrated regional- and network-based measures under an ensemble machine learning model based on structural MRI data. We systematically assessed whether IBRAIN could be a neuroimaging biomarker for AD., Findings: IBRAIN accurately differentiated individuals with AD from NCs ( AUC  = 0.92) and other neurodegenerative diseases, including Frontotemporal dementia (FTD), Parkinson's disease (PD), Vascular dementia (VaD) and Amyotrophic Lateral Sclerosis (ALS) ( AUC  = 0.92). IBRAIN was significantly correlated to clinical measures and gene expression, enriched in immune process and protein metabolism. The IBRAIN score exhibited a significant ability to reveal the distinct progression of prodromal AD (i.e., Mild cognitive impairment, MCI) (Hazard Ratio ( HR ) = 6.52 [95% CI: 4.42∼9.62], p  < 1 × 10 -16 ), which offers similar powerful performance with Cerebrospinal Fluid (CSF) Aβ ( HR  = 3.78 [95% CI: 2.63∼5.43], p  = 2.13 × 10 -14 ) and CSF Tau ( HR  = 3.77 [95% CI: 2.64∼5.39], p  = 9.53 × 10 -15 ) based on the COX and Log-rank test. Notably, the IBRAIN shows comparable sensitivity ( beta  = -0.70, p  < 1 × 10 -16 ) in capturing longitudinal changes in individuals with conversion to AD than CSF Aβ ( beta  = -0.26, p  = 4.40 × 10 -9 ) and CSF Tau ( beta  = 0.12, p  = 1.02 × 10 -5 )., Interpretation: Our findings suggested that IBRAIN is a biologically relevant, specific, and sensitive neuroimaging biomarker that can serve as a clinical measure to uncover prodromal AD progression. It has strong potential for application in future clinical practice and treatment trials., Funding: Science and Technology Innovation 2030 Major Projects, the National Natural Science Foundation of China, Beijing Natural Science Funds, the Fundamental Research Funds for the CentralUniversity, and the Startup Funds for Talents at Beijing Normal University., Competing Interests: The authors report no biomedical financial interests or potential conflicts of interest., (© 2023 The Author(s).)

Glioblastoma (GBM) is the most common primary brain tumor. It is highly malignant and has a correspondingly poor prognosis. Diagnosis and monitoring are mainly accomplished with MRI, but remain challenging in some cases. Therefore, complementary methods for tumor detection and characterization would be beneficial. Using magnetic resonance elastography (MRE), we performed a longitudinal study of the biomechanical properties of intracranially implanted GBM in mice and compared the results to histopathology. The biomechanical parameters of viscoelastic modulus, shear wave speed and phase angle were significantly lower in tumors compared with healthy brain tissue and decreased over time with tumor progression. Moreover, some MRE parameters revealed sub‐regions at later tumor stages, which were not easily detectable on anatomical MRI images. Comparison with histopathology showed that softer tumor regions contained necrosis and patches of viable tumor cells. In contrast, areas of densely packed tumor cells and blood vessels identified with histology coincided with higher values of viscoelastic modulus and shear wave speed. Interestingly, the phase angle was independent from these anatomical variations. In summary, MRE depicted longitudinal and morphological changes in GBM and may prove valuable for tumor characterization in patients. [ABSTRACT FROM AUTHOR]

Although white matter hyperintensities (WMH), markers of cerebral small vessel disease (SVD), are believed to generally increase over time, some studies have shown sharp decreases after therapeutic intervention, suggesting that WMH progression may be more dynamic than previously thought. Our primary goal was to examine dynamic progression of WMH in a real-world sample of Alzheimer's disease (AD) patients and normal elderly (NC), with varying degrees of SVD. WMH volumes from serial magnetic resonance imaging (MRI; mean = 1.8 years) were measured from NC (n = 44) and AD patients (n = 113) with high and low SVD burden. Dynamic progression for each individual was measured using spatial overlap images to assess shrinkage, growth, and stable WMH volumes. Significant group differences were found for shrinkage (p < 0.001), growth (p < 0.001) and stable (p < 0.001) WMH, where the AD high SVD group showed the largest changes relative to low SVD and NC. Our results suggest spatial progression measured at the individual patient level may be more sensitive to the dynamic nature of WMH. [ABSTRACT FROM AUTHOR]

Prodromal Alzheimer's disease (AD) is a neurodegenerative condition typically progressing to dementia within 3 years. We describe a case of a mild cognitive impairment (MCI) patient with biomarker evidence for amyloidosis, tau, and neurodegeneration who had minimal changes in clinical phenotype during an 11-year period. AD biomarkers were obtained with cerebrospinal fluid analysis and amyloid PET imaging, both of which supported a biological diagnosis of AD. However, the patient's neuropsychological profile remained stable over 11 years except for mild memory-retrieval changes. This case provides evidence that MCI with supportive AD biomarkers may have an atypically minimal progression., Competing Interests: The authors have no conflict of interest to report., (© 2023 – The authors. Published by IOS Press.)

Background: The diagnosis of the behavioural variant of frontotemporal dementia (bvFTD) can be challenging. At present there is a paucity of prospective work addressing the specificity of current diagnostic criteria for bvFTD with respect to long-term outcome (i.e., false positives versus true positives). Methods: Here we report two individuals who met current clinical criteria for bvFTD and who underwent detailed long-term clinical and neuropsychological follow-up. In addition, both had serial volumetric MRI and functional metabolic (FDG-PET) imaging separated by 5 years. Results: One case had a slow clinical decline as well as both progressive atrophy and hypometabolism in a frontotemporal distribution, consistent with a neurodegenerative FTD syndrome. However, the second developed neither atrophy nor hypometabolism and remained clinically stable, a decade from symptom onset. Conclusion: We propose that these cases illustrate that while there may be a slow evolution in bvFTD, it is possible that some cases who meet current criteria may not have a neurodegenerative syndrome. If correct, this hypothesis has important implications for the current diagnostic criteria. A potential hierarchy for diagnostic certainty in bvFTD is suggested. [ABSTRACT FROM AUTHOR]

Aim: To determine the longitudinal progression of fractures through a population of osteoporotic women with no existing vertebral fractures. Methods: The probability of having one or more vertebral fractures in the next year given a current status of 0-13 existing vertebral fractures was estimated using data from control patients of an osteoporosis clinical trial program. Fracture probabilities were used to form a transition matrix that models the change in fracture state from one year to the next. A Markov model was used to show the distribution of fracture prevalence over time for a population of women with osteoporosis but, initially, with no existing vertebral fractures. Results: An osteoporotic woman without existing vertebral fractures has a 7.7% chance (95% CI, 5.8% to 9.9%) of having a vertebral fracture within 1 year. After 5 years, 33% (95% CI, 25% to 41%) will have developed vertebral fractures, of which 11% (95% CI, 8% to 16%) will have =2 fractures. After 10 years, 55% (95% CI, 44% to 65%) will have developed vertebral fractures, of which 29% (95% CI, 22% 37%) will have =2 fractures. Each 1% absolute reduction in the annual first-fracture risk corresponds to an approximate 4% reduction in the 5-year fracture incidence. Therefore, reducing the risk of first fracture from 8% to 2% reduces the 5-year fracture incidence from ~34% to ~10%. Conclusions: Fracture prevalence rapidly increases over time in a population of osteoporotic women despite treatment with calcium and vitamin D supplements. Identifying and treating patients at risk of fracture, but who have not yet sustained a fracture, will substantially reduce the long-term burden of osteoporosis. [ABSTRACT FROM AUTHOR]