Combined Connectomics, MAPT Gene Expression, and Amyloid Deposition to Explain Regional Tau Deposition in Alzheimer Disease.

OBJECTIVE: We aimed to test whether region-specific factors, including spatial expression patterns of the tau-encoding gene MAPT and regional levels of amyloid positron emission tomography (PET), enhance connectivity-based modeling of the spatial variability in tau-PET deposition in the Alzheimer disease (AD) spectrum. METHODS: We included 685 participants (395 amyloid-positive participants within AD spectrum and 290 amyloid-negative controls) with tau-PET and amyloid-PET from 3 studies (Alzheimer's Disease Neuroimaging Initiative, 18 F-AV-1451-A05, and BioFINDER-1). Resting-state functional magnetic resonance imaging was obtained in healthy controls (n = 1,000) from the Human Connectome Project, and MAPT gene expression from the Allen Human Brain Atlas. Based on a brain-parcellation atlas superimposed onto all modalities, we obtained region of interest (ROI)-to-ROI functional connectivity, ROI-level PET values, and MAPT gene expression. In stepwise regression analyses, we tested connectivity, MAPT gene expression, and amyloid-PET as predictors of group-averaged and individual tau-PET ROI values in amyloid-positive participants. RESULTS: Connectivity alone explained 21.8 to 39.2% (range across 3 studies) of the variance in tau-PET ROI values averaged across amyloid-positive participants. Stepwise addition of MAPT gene expression and amyloid-PET increased the proportion of explained variance to 30.2 to 46.0% and 45.0 to 49.9%, respectively. Similarly, for the prediction of patient-level tau-PET ROI values, combining all 3 predictors significantly improved the variability explained (mean adjusted R2 range across studies = 0.118-0.148, 0.156-0.196, and 0.251-0.333 for connectivity alone, connectivity plus MAPT expression, and all 3 modalities combined, respectively). INTERPRETATION: Across 3 study samples, combining the functional connectome and molecular properties substantially enhanced the explanatory power compared to single modalities, providing a valuable tool to explain regional susceptibility to tau deposition in AD. ANN NEUROL 2024;95:274-287.

Magnetic resonance imaging measures of brain volumes across the EXPEDITION trials in mild and moderate Alzheimer's disease dementia.

Introduction: Solanezumab is a monoclonal antibody that preferentially binds soluble amyloid beta and promotes its clearance from the brain. The aim of this post hoc analysis was to assess the effect of low-dose solanezumab (400 mg) on global brain volume measures in patients with mild or moderate Alzheimer's disease (AD) dementia quantified using volumetric magnetic resonance imaging (vMRI) data from the EXPEDITION clinical trial program. Methods: Patients with mild or moderate AD (EXPEDITION and EXPEDITION2) and mild AD (EXPEDITION3), were treated with either placebo or solanezumab (400 mg) every 4 weeks (Q4W) for 76 weeks. vMRI scans were acquired at baseline and at 80 weeks from 427 MRI facilities using a standardized imaging protocol. Whole brain volume (WBV) and ventricle volume (VV) changes were estimated at 80 weeks using either boundary shift integral (EXPEDITION and EXPEDITION2) or tensor-based morphometry (EXPEDITION3). Results: The pooled cohort used for this study consisted of participants with vMRI at baseline and week 80 across the three trials. Analyzed patient subgroups comprised full patient cohort (N = 2933), apolipoprotein E (APOE) ε4+ carriers (N = 1835), and patients with mild (N = 2497) or moderate AD dementia (N = 428). No significant effect (all P-values ≥.05) of treatment was observed in the pooled sample, individual trials, or subgroups of patients with mild or moderate AD or APOE ε4 carriers, in either WBV or VV change. Discussion: Analysis of patients with mild or moderate AD dementia from baseline to 80 weeks using vMRI measures of WBV and VV changes suggested that low-dose solanezumab was not linked to changes in volumes at 80 weeks. Analysis of the pooled cohort did not demonstrate an effect on brain volumes with treatment. Evaluation of a higher dose of solanezumab in the preclinical stage of AD is currently being undertaken.

Predicting the Emergence of Major Neurocognitive Disorder Within Three Months After a Stroke.

Background: Neurocognitive disorder (NCD) is common after stroke, with major NCD appearing in about 10% of survivors of a first-ever stroke. We aimed to classify clinical- and imaging factors related to rapid development of major NCD 3 months after a stroke, so as to examine the optimal composition of factors for predicting rapid development of the disorder. We hypothesized that the prediction would mainly be driven by neurodegenerative as opposed to vascular brain changes. Methods: Stroke survivors from five Norwegian hospitals were included from the "Norwegian COgnitive Impairment After STroke" (Nor-COAST) study. A support vector machine (SVM) classifier was trained to distinguish between patients who developed major NCD 3 months after the stroke and those who did not. Potential predictor factors were based on previous literature and included both vascular and neurodegenerative factors from clinical and structural magnetic resonance imaging findings. Cortical thickness was obtained via FreeSurfer segmentations, and volumes of white matter hyperintensities (WMH) and stroke lesions were semi-automatically gathered using FSL BIANCA and ITK-SNAP, respectively. The predictive value of the classifier was measured, compared between classifier models and cross-validated. Results: Findings from 227 stroke survivors [age = 71.7 (11.3), males = (56.4%), stroke severity NIHSS = 3.8 (4.8)] were included. The best predictive accuracy (AUC = 0.876) was achieved by an SVM classifier with 19 features. The model with the fewest number of features that achieved statistically comparable accuracy (AUC = 0.850) was the 8-feature model. These features ranked by their weighting were; stroke lesion volume, WMH volume, left occipital and temporal cortical thickness, right cingulate cortical thickness, stroke severity (NIHSS), antiplatelet medication intake, and education. Conclusion: The rapid (<3 months) development of major NCD after stroke is possible to predict with an 87.6% accuracy and seems dependent on both neurodegenerative and vascular factors, as well as aspects of the stroke itself. In contrast to previous literature, we also found that vascular changes are more important than neurodegenerative ones. Although possible to predict with relatively high accuracy, our findings indicate that the development of rapid onset post-stroke NCD may be more complex than earlier suggested.

Lanabecestat: Neuroimaging results in early symptomatic Alzheimer's disease.

INTRODUCTION: Lanabecestat, a beta-site amyloid precursor protein-cleaving enzyme 1 (BACE1) inhibitor, was investigated as a potential Alzheimer's disease (AD)-modifying treatment. As previously reported, amyloid beta (Aß) neuritic plaque burden reduction did not result in clinical benefit. Lanabecestat's effects on neuroimaging biomarkers and correlations between neuroimaging biomarkers and efficacy measures are reported. METHODS: AMARANTH and DAYBREAK-ALZ were 104- and 78-week, multicenter, randomized, double-blind, placebo-controlled studies of lanabecestat in early symptomatic AD (AMARANTH) and mild AD dementia (DAYBREAK-ALZ). Patients randomly (1:1:1) received placebo, lanabecestat 20 mg, or lanabecestat 50 mg daily (AMARANTH, n = 2218; DAYBREAK-ALZ, n = 1722). Florbetapir positron emission tomography (PET), fluorodeoxyglucose (FDG) PET, flortaucipir PET, and volumetric magnetic resonance imaging (MRI) were used to measure Aß neuritic plaque burden, cerebral metabolism, aggregated tau neurofibrillary tangles, and brain volume, respectively. Additionally, florbetapir perfusion scans were performed in DAYBREAK-ALZ. Efficacy measures included 13-item Alzheimer's Disease Assessment Scale-Cognitive Subscale, Alzheimer's Disease Cooperative Study Activities of Daily Living Inventory, Clinical Dementia Rating-Sum of Boxes, Functional Activities Questionnaire, and Mini-Mental State Examination. These studies stopped early due to futility. RESULTS: Despite previously observed annualized reduction in Aß neuritic plaque burden, there were no treatment differences in annualized change of aggregated tau neurofibrillary tangle burden (AMARANTH, n = 284; DAYBREAK-ALZ, n = 70), cerebral metabolism (AMARANTH, n = 260; DAYBREAK-ALZ, n = 38) and perfusion (DAYBREAK-ALZ, n = 213). Greater brain volume reduction (AMARANTH, n = 1697 [whole brain]; DAYBREAK-ALZ, n = 650 [whole brain]) occurred on lanabecestat compared to placebo. Higher baseline aggregated tau neurofibrillary tangle burden, lower cerebral metabolism, and lower brain volumes correlated with poorer baseline efficacy scores and greater clinical worsening. Lower baseline cerebral perfusion correlated with poorer baseline efficacy scores. Reduction in cerebral metabolism or whole brain volume correlated with clinical worsening, regardless of treatment assignment. DISCUSSION: Tau pathology and cerebral metabolism assessments showed no evidence of lanabecestat slowing pathophysiologic progression of AD. Lanabecestat exposure was associated with brain volume reductions. Correlations between imaging measures and cognitive assessments may aid future study design.

GEFF: Graph embedding for functional fingerprinting.

It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. Thus, while designing an experiment, one could choose a task fMRI to ask a specific question and combine it with resting-state fMRI to extract maximum subject differentiability using GEFF. In addition to subject-identification, GEFF was also used for identification of cognitive states, i.e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent task-identification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task- and subject-dominant, subject-dominant or neither, using two-way ANOVA of their corresponding loadings, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states.