Presenilin-1 mutation position influences amyloidosis, small vessel disease, and dementia with disease stage.

INTRODUCTION: Amyloidosis, including cerebral amyloid angiopathy, and markers of small vessel disease (SVD) vary across dominantly inherited Alzheimer's disease (DIAD) presenilin-1 (PSEN1) mutation carriers. We investigated how mutation position relative to codon 200 (pre-/postcodon 200) influences these pathologic features and dementia at different stages. METHODS: Individuals from families with known PSEN1 mutations (n = 393) underwent neuroimaging and clinical assessments. We cross-sectionally evaluated regional Pittsburgh compound B-positron emission tomography uptake, magnetic resonance imaging markers of SVD (diffusion tensor imaging-based white matter injury, white matter hyperintensity volumes, and microhemorrhages), and cognition. RESULTS: Postcodon 200 carriers had lower amyloid burden in all regions but worse markers of SVD and worse Clinical Dementia Rating® scores compared to precodon 200 carriers as a function of estimated years to symptom onset. Markers of SVD partially mediated the mutation position effects on clinical measures. DISCUSSION: We demonstrated the genotypic variability behind spatiotemporal amyloidosis, SVD, and clinical presentation in DIAD, which may inform patient prognosis and clinical trials. HIGHLIGHTS: Mutation position influences Aß burden, SVD, and dementia. PSEN1 pre-200 group had stronger associations between Aß burden and disease stage. PSEN1 post-200 group had stronger associations between SVD markers and disease stage. PSEN1 post-200 group had worse dementia score than pre-200 in late disease stage. Diffusion tensor imaging-based SVD markers mediated mutation position effects on dementia in the late stage.

Plasma VEGFA and PGF impact longitudinal tau and cognition in preclinical Alzheimer's disease.

Vascular dysfunction is increasingly recognized as an important contributor to the pathogenesis of Alzheimer's disease. Alterations in vascular endothelial growth factor (VEGF) pathways have been implicated as potential mechanisms. However, the specific impact of VEGF proteins in preclinical Alzheimer's disease and their relationships with other Alzheimer's disease and vascular pathologies during this critical early period remain to be elucidated. We included 317 older adults from the Harvard Aging Brain Study, a cohort of individuals who were cognitively unimpaired at baseline and followed longitudinally for up to 12 years. Baseline VEGF family protein levels (VEGFA, VEGFC, VEGFD, PGF and FLT1) were measured in fasting plasma using high-sensitivity immunoassays. Using linear mixed effects models, we examined the interactive effects of baseline plasma VEGF proteins and amyloid PET burden (Pittsburgh Compound-B) on longitudinal cognition (Preclinical Alzheimer Cognitive Composite-5). We further investigated if effects on cognition were mediated by early neocortical tau accumulation (flortaucipir PET burden in the inferior temporal cortex) or hippocampal atrophy. Lastly, we examined the impact of adjusting for baseline cardiovascular risk score or white matter hyperintensity volume. Baseline plasma VEGFA and PGF each showed a significant interaction with amyloid burden on prospective cognitive decline. Specifically, low VEGFA and high PGF were associated with greater cognitive decline in individuals with elevated amyloid, i.e. those on the Alzheimer's disease continuum. Concordantly, low VEGFA and high PGF were associated with accelerated longitudinal tau accumulation in those with elevated amyloid. Moderated mediation analyses confirmed that accelerated tau accumulation fully mediated the effects of low VEGFA and partially mediated (31%) the effects of high PGF on faster amyloid-related cognitive decline. The effects of VEGFA and PGF on tau and cognition remained significant after adjusting for cardiovascular risk score or white matter hyperintensity volume. There were concordant but non-significant associations with longitudinal hippocampal atrophy. Together, our findings implicate low VEGFA and high PGF in accelerating early neocortical tau pathology and cognitive decline in preclinical Alzheimer's disease. Additionally, our results underscore the potential of these minimally-invasive plasma biomarkers to inform the risk of Alzheimer's disease progression in the preclinical population. Importantly, VEGFA and PGF appear to capture distinct effects from vascular risks and cerebrovascular injury. This highlights their potential as new therapeutic targets, in combination with anti-amyloid and traditional vascular risk reduction therapies, to slow the trajectory of preclinical Alzheimer's disease and delay or prevent the onset of cognitive decline.

Etiology of White Matter Hyperintensities in Autosomal Dominant and Sporadic Alzheimer Disease.

Importance: Increased white matter hyperintensity (WMH) volume is a common magnetic resonance imaging (MRI) finding in both autosomal dominant Alzheimer disease (ADAD) and late-onset Alzheimer disease (LOAD), but it remains unclear whether increased WMH along the AD continuum is reflective of AD-intrinsic processes or secondary to elevated systemic vascular risk factors. Objective: To estimate the associations of neurodegeneration and parenchymal and vessel amyloidosis with WMH accumulation and investigate whether systemic vascular risk is associated with WMH beyond these AD-intrinsic processes. Design, Setting, and Participants: This cohort study used data from 3 longitudinal cohort studies conducted in tertiary and community-based medical centers-the Dominantly Inherited Alzheimer Network (DIAN; February 2010 to March 2020), the Alzheimer's Disease Neuroimaging Initiative (ADNI; July 2007 to September 2021), and the Harvard Aging Brain Study (HABS; September 2010 to December 2019). Main Outcome and Measures: The main outcomes were the independent associations of neurodegeneration (decreases in gray matter volume), parenchymal amyloidosis (assessed by amyloid positron emission tomography), and vessel amyloidosis (evidenced by cerebral microbleeds [CMBs]) with cross-sectional and longitudinal WMH. Results: Data from 3960 MRI sessions among 1141 participants were included: 252 pathogenic variant carriers from DIAN (mean [SD] age, 38.4 [11.2] years; 137 [54%] female), 571 older adults from ADNI (mean [SD] age, 72.8 [7.3] years; 274 [48%] female), and 318 older adults from HABS (mean [SD] age, 72.4 [7.6] years; 194 [61%] female). Longitudinal increases in WMH volume were greater in individuals with CMBs compared with those without (DIAN: t = 3.2 [P = .001]; ADNI: t = 2.7 [P = .008]), associated with longitudinal decreases in gray matter volume (DIAN: t = -3.1 [P = .002]; ADNI: t = -5.6 [P < .001]; HABS: t = -2.2 [P = .03]), greater in older individuals (DIAN: t = 6.8 [P < .001]; ADNI: t = 9.1 [P < .001]; HABS: t = 5.4 [P < .001]), and not associated with systemic vascular risk (DIAN: t = 0.7 [P = .40]; ADNI: t = 0.6 [P = .50]; HABS: t = 1.8 [P = .06]) in individuals with ADAD and LOAD after accounting for age, gray matter volume, CMB presence, and amyloid burden. In older adults without CMBs at baseline, greater WMH volume was associated with CMB development during longitudinal follow-up (Cox proportional hazards regression model hazard ratio, 2.63; 95% CI, 1.72-4.03; P < .001). Conclusions and Relevance: The findings suggest that increased WMH volume in AD is associated with neurodegeneration and parenchymal and vessel amyloidosis but not with elevated systemic vascular risk. Additionally, increased WMH volume may represent an early sign of vessel amyloidosis preceding the emergence of CMBs.

Automated generation of cerebral blood flow and arterial transit time maps from multiple delay arterial spin-labeled MRI.

PURPOSE: Develop and evaluate a deep learning approach to estimate cerebral blood flow (CBF) and arterial transit time (ATT) from multiple post-labeling delay (PLD) ASL MRI. METHODS: ASL MRI were acquired with 6 PLDs on a 1.5T or 3T GE system in adults with and without cognitive impairment (N = 99). Voxel-level CBF and ATT maps were quantified by training models with distinct convolutional neural network architectures: (1) convolutional neural network (CNN) and (2) U-Net. Models were trained and compared via 5-fold cross validation. Performance was evaluated using mean absolute error (MAE). Model outputs were trained on and compared against a reference ASL model fitting after data cleaning. Minimally processed ASL data served as another benchmark. Model output uncertainty was estimated using Monte Carlo dropout. The better-performing neural network was subsequently re-trained on inputs with missing PLDs to investigate generalizability to different PLD schedules. RESULTS: Relative to the CNN, the U-Net yielded lower MAE on training data. On test data, the U-Net MAE was 8.4 ± 1.4 mL/100 g/min for CBF and 0.22 ± 0.09 s for ATT. A significant association was observed between MAE and Monte Carlo dropout-based uncertainty estimates. Neural network performance remained stable despite systematically reducing the number of input images (i.e., up to 3 missing PLD images). Mean processing time was 10.77 s for the U-Net neural network compared to 10 min 41 s for the reference pipeline. CONCLUSION: It is feasible to generate CBF and ATT maps from 1.5T and 3T multi-PLD ASL MRI with a fast deep learning image-generation approach.

Assessment of cognitive and neural recovery in survivors of pediatric brain tumors in a pilot clinical trial using metformin.

We asked whether pharmacological stimulation of endogenous neural precursor cells (NPCs) may promote cognitive recovery and brain repair, focusing on the drug metformin, in parallel rodent and human studies of radiation injury. In the rodent cranial radiation model, we found that metformin enhanced the recovery of NPCs in the dentate gyrus, with sex-dependent effects on neurogenesis and cognition. A pilot double-blind, placebo-controlled crossover trial was conducted (ClinicalTrials.gov, NCT02040376) in survivors of pediatric brain tumors who had been treated with cranial radiation. Safety, feasibility, cognitive tests and MRI measures of white matter and the hippocampus were evaluated as endpoints. Twenty-four participants consented and were randomly assigned to complete 12-week cycles of metformin (A) and placebo (B) in either an AB or BA sequence with a 10-week washout period at crossover. Blood draws were conducted to monitor safety. Feasibility was assessed as recruitment rate, medication adherence and procedural adherence. Linear mixed modeling was used to examine cognitive and MRI outcomes as a function of cycle, sequence and treatment. We found no clinically relevant safety concerns and no serious adverse events associated with metformin. Sequence effects were observed for all cognitive outcomes in our linear mixed models. For the subset of participants with complete data in cycle 1, metformin was associated with better performance than placebo on tests of declarative and working memory. We present evidence that a clinical trial examining the effects of metformin on cognition and brain structure is feasible in long-term survivors of pediatric brain tumors and that metformin is safe to use and tolerable in this population. This pilot trial was not intended to test the efficacy of metformin for cognitive recovery and brain growth, but the preliminary results are encouraging and warrant further investigation in a large multicenter phase 3 trial.

Amyloid-beta burden predicts prospective decline in body mass index in clinically normal adults.

In the present study, we tested the hypothesis that higher amyloid-beta (Aß) burden at baseline is associated with greater longitudinal decline in body mass index (BMI) in clinically normal adults. Participants from the Harvard Aging Brain Study (n = 312) and the Alzheimer's Disease Neuroimaging Initiative (n = 336) underwent Aß positron emission tomography at baseline. BMI was assessed longitudinally over a median of >4 years. Linear mixed models showed that higher baseline Aß burden was significantly associated with greater decline in BMI in both the Harvard Aging Brain Study (t = -1.93; p = 0.05) and Alzheimer's Disease Neuroimaging Initiative cohorts (t = -2.54; p = 0.01), after adjusting for covariates, including cognitive performance and depressive symptoms. In addition, the association of Aß burden with longitudinal decline in BMI persisted in both cohorts after excluding participants with diabetes/endocrine disturbances and participants classified as underweight or obese (BMI <18.5 or >30). These findings suggest that decline in BMI in clinically normal adults may be an early manifestation related to cerebral amyloidosis that precedes objective cognitive impairment. Therefore, unintentional BMI decline in otherwise healthy individuals might alert clinicians to increased risk of Alzheimer's disease.

Toward in vivo lung's tissue incompressibility characterization for tumor motion modeling in radiation therapy.

PURPOSE: A novel technique is proposed to characterize lung tissue incompressibility variation during respiration. Estimating lung tissue incompressibility parameter variations resulting from air content variation throughout respiration is critical for computer assisted tumor motion tracking. Continuous tumor motion is a major challenge in lung cancer radiotherapy, especially with external beam radiotherapy. If not accounted for, this motion may lead to areas of radiation overdosage for normal tissue. Given the unavailability of imaging modality that can be used effectively for real-time lung tumor tracking, computer assisted approach based on tissue deformation estimation can be a good alternative. This approach involves lung biomechanical model where its fidelity depends on input tissue properties. This investigation shows that considering variable tissue incompressibility parameter is very important for predicting tumor motion accurately, hence improving the lung radiotherapy outcome. METHODS: First, an in silico lung phantom study was conducted to demonstrate the importance of employing variable Poisson's ratio for tumor motion predication. After it was established that modeling this variability is critical for accurate tumor motion prediction, an optimization based technique was developed to estimate lung tissue Poisson's ratio as a function of respiration cycle time. In this technique, the Poisson's ratio and lung pressure value were varied systematically until optimal values were obtained, leading to maximum similarity between acquired and simulated 4D CT lung images. This technique was applied in an ex vivo porcine lung study where simulated images were constructed using the end exhale CT image and deformation fields obtained from the lung's FE modeling of each respiration time increment. To model the tissue, linear elastic and Marlow hyperelastic material models in conjunction with variable Poisson's ratio were used. RESULTS: The phantom study showed that the tumor motion trajectory and its final locations obtained from simulations with and without considering tissue incompressibility variation were very different. For example, tumor displacements in the z direction were -11.23 and -38.10 mm obtained with the Marlow hyperelastic material model in conjunction with constant and variable Poisson's ratio, respectively. By comparing the acquired 4D-CT image sequence of the porcine lung with their image sequence counterparts obtained from the hyperelastic model with constant and variable Poisson's ratio, it was shown that using variable tissue incompressibility reduced errors significantly in tumor motion prediction. CONCLUSIONS: This investigation demonstrates the importance of incompressibility variation estimation and utilization for accurate tumor tracking in computer assisted lung external beam radiation therapy. An optimization framework was developed to estimate a Poisson's ratio function in terms of respiration cycle time using experimental image data of the lung. Utilizing this function along with respiratory system FE modeling may lead to more effective tumor targeting, hence potentially improving the outcome of lung external beam radiation therapy techniques. This is particularly true for stereotactic body radiation therapy where only one or a few fraction treatments are applied, precluding the possibility of averaging out dosimetric deviations introduced by the respiratory motion.

Towards modeling tumor motion in the deflated lung for minimally invasive ablative procedures.

A computational model is proposed to demonstrate the feasibility of characterizing the motion of lung tumors caused by respiratory diaphragm forces using a tissue biomechanics approach. Compensating for such motion is very important for developing effective systems of minimally invasive tumor ablative procedures, e.g., Low Dose Rate (LDR) lung brachytherapy. To minimize the effects of respiratory motion, the target lung is almost completely deflated before starting such procedures. However, a significant amount of motion persists in the target lung due to the diaphragm contact forces required for the other lung's respiration. In this study, a model pipeline was developed which inputs a pre-operative 4D-CT image sequence of the lung to output the predicted 3D motion trajectory of the tumor over the respiratory cycle. A finite element method was used in this pipeline to model the lung tissue deformation in order to predict the tumor motion. Experiments were conducted on an ex vivo porcine lung to demonstrate the performance and assess the accuracy of the proposed pipeline. The resultant tumor motion trajectory obtained from the biomechanical model of the lung was compared to the experimental trajectory obtained from CT imaging. Results were promising, suggesting that tissue mechanics-based modeling can be employed for effective characterization of lung tumor respiratory motion to improve accuracy in lung tumor ablative procedures.