Assessment of Efficacy and Safety of Zagotenemab: Results From PERISCOPE-ALZ, a Phase 2 Study in Early Symptomatic Alzheimer Disease.

BACKGROUND AND OBJECTIVES: Zagotenemab (LY3303560), a monoclonal antibody that preferentially targets misfolded, extracellular, aggregated tau, was assessed in the PERISCOPE-ALZ phase 2 study to determine its ability to slow cognitive and functional decline relative to placebo in early symptomatic Alzheimer disease (AD). METHODS: Participants were enrolled across 56 sites in North America and Japan. Key eligibility criteria included age of 60-85 years, Mini-Mental State Examination score of 20-28, and intermediate levels of brain tau on PET imaging. In this double-blind study, participants were equally randomized to 1,400 mg or 5,600 mg of zagotenemab, or placebo (IV infusion every 4 weeks for 100 weeks). The primary outcome was change on the Integrated AD Rating Scale (iADRS) assessed by a Bayesian Disease Progression model. Secondary measures include mixed model repeated measures analysis of additional cognitive and functional endpoints as well as biomarkers of AD pathology. RESULTS: A total of 360 participants (mean age = 75.4 years; female = 52.8%) were randomized, and 218 completed the treatment period. Demographics and baseline characteristics were reasonably balanced among arms. The mean disease progression ratio (proportional decline in the treated vs placebo group) with 95% credible intervals for the iADRS was 1.10 (0.959-1.265) for the zagotenemab low-dose group and 1.05 (0.907-1.209) for the high-dose, where a ratio less than 1 favors the treatment group. Secondary clinical endpoint measures failed to show a drug-placebo difference in favor of zagotenemab. No treatment effect was demonstrated by flortaucipir PET, volumetric MRI, or neurofilament light chain (NfL) analyses. A dose-related increase in plasma phosphorylated tau181 and total tau was demonstrated. Zagotenemab treatment groups reported a higher incidence of adverse events (AEs) (85.1%) compared with the placebo group (74.6%). This difference was not attributable to any specific AE or category of AEs. DISCUSSION: In participants with early symptomatic AD, zagotenemab failed to achieve significant slowing of clinical disease progression compared with placebo. Imaging biomarker and plasma NfL findings did not show evidence of pharmacodynamic activity or disease modification. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov: NCT03518073. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that for patients with early symptomatic AD, zagotenemab does not slow clinical disease progression.

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.

ApoE4 and Connectivity-Mediated Spreading of Tau Pathology at Lower Amyloid Levels.

Importance: For the Alzheimer disease (AD) therapies to effectively attenuate clinical progression, it may be critical to intervene before the onset of amyloid-associated tau spreading, which drives neurodegeneration and cognitive decline. Time points at which amyloid-associated tau spreading accelerates may depend on individual risk factors, such as apolipoprotein E ε4 (ApoE4) carriership, which is linked to faster disease progression; however, the association of ApoE4 with amyloid-related tau spreading is unclear. Objective: To assess if ApoE4 carriers show accelerated amyloid-related tau spreading and propose amyloid positron emission tomography (PET) thresholds at which tau spreading accelerates in ApoE4 carriers vs noncarriers. Design, Setting, and Participants: This cohort study including combined ApoE genotyping, amyloid PET, and longitudinal tau PET from 2 independent samples: the Alzheimer's Disease Neuroimaging Initiative (ADNI; n = 237; collected from April 2015 to August 2022) and Avid-A05 (n = 130; collected from December 2013 to July 2017) with a mean (SD) tau PET follow-up time of 1.9 (0.96) years in ADNI and 1.4 (0.23) years in Avid-A05. ADNI is an observational multicenter Alzheimer disease neuroimaging initiative and Avid-A05 an observational clinical trial. Participants classified as cognitively normal (152 in ADNI and 77 in Avid-A05) or mildly cognitively impaired (107 in ADNI and 53 in Avid-A05) were selected based on ApoE genotyping, amyloid-PET, and longitudinal tau PET data availability. Participants with ApoE ε2/ε4 genotype or classified as having dementia were excluded. Resting-state functional magnetic resonance imaging connectivity templates were based on 42 healthy participants in ADNI. Main Outcomes and Measures: Mediation of amyloid PET on the association between ApoE4 status and subsequent tau PET increase through Braak stage regions and interaction between ApoE4 status and amyloid PET with annual tau PET increase through Braak stage regions and connectivity-based spreading stages (tau epicenter connectivity ranked regions). Results: The mean (SD) age was 73.9 (7.35) years among the 237 ADNI participants and 70.2 (9.7) years among the 130 Avid-A05 participants. A total of 107 individuals in ADNI (45.1%) and 45 in Avid-A05 (34.6%) were ApoE4 carriers. Across both samples, we found that higher amyloid PET-mediated ApoE4-related tau PET increased globally (ADNI b, 0.15; 95% CI, 0.05-0.28; P = .001 and Avid-A05 b, 0.33; 95% CI, 0.14-0.54; P < .001) and in earlier Braak regions. Further, we found a significant association between ApoE4 status by amyloid PET interaction and annual tau PET increases consistently through early Braak- and connectivity-based stages where amyloid-related tau accumulation was accelerated in ApoE4carriers vs noncarriers at lower centiloid thresholds, corrected for age and sex. Conclusions and Relevance: The findings in this study indicate that amyloid-related tau accumulation was accelerated in ApoE4 carriers at lower amyloid levels, suggesting that ApoE4 may facilitate earlier amyloid-driven tau spreading across connected brain regions. Possible therapeutic implications might be further investigated to determine when best to prevent tau spreading and thus cognitive decline depending on ApoE4 status.

Tau as a diagnostic instrument in clinical trials to predict amyloid in Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) is characterized by the presence of both amyloid and tau pathology. In vivo diagnosis can be made with amyloid and tau positron emission tomography (PET) imaging. Emergent evidence supports that amyloid and tau accumulation are associated and that amyloid accumulation may precede that of tau. This report further investigates the relationship between amyloid and tau to assess whether elevated cortical tau can predict elevated amyloid in participants with early symptomatic AD. METHODS: Florbetapir F18 and flortaucipir F18 uptake were evaluated from baseline PET scans collected in three multi-center studies with cognitively impaired participants, including A05 (N = 306; NCT02016560), TB (N = 310; TRAILBLAZER-ALZ; NCT03367403), and TB2 (N = 1165; TRAILBLAZER-ALZ 2; NCT04437511). Images were assessed using visual and quantitative approaches to establish amyloid (A+) and tau (T+) positivity, as well as a combination method (tauVQ) to establish T+. Associations between global amyloid and tau were evaluated with positive and negative predictive values (PPV, NPV) and likelihood ratios (LR+, LR-). Predictive values within subgroups according to ethnicity, race, cognitive score, age, and sex were also evaluated. The relationship between regional tau (four target and two reference regions were tested) and global amyloid was investigated in A05 participant scans using receiver-operating characteristic (ROC) curves. RESULTS: PPV for amyloid positivity was ≥93% for all three trials using various A+ and T+ definitions, including visual, quantitative, and combination methods. Population characteristics did not have an impact on A+ predictability. Regional analyses (early tau (Eτ) volume of interest (VOI), temporal, parietal, frontal) revealed significant area under the ROC curve in Eτ VOI compared to frontal region, regardless of reference region and consistent among visual and quantitative A+ definitions (p < 0.001). DISCUSSION: These findings suggest that a positive tau PET scan is associated (≥93%) with amyloid positivity in individuals with early symptomatic AD, with the potential benefits of reducing clinical trial and health care expenses, radiation exposure, and participant time. Highlights: Positron emission tomography (PET) evaluates candidates for Alzheimer's disease (AD) research. A positive tau PET scan is associated (≥93%) with amyloid positivity.A positive amyloid PET is not necessarily associated with tau positivity.Tau PET could be the sole diagnostic tool to confirm candidates for AD trials.

Longitudinal head-to-head comparison of 11C-PiB and 18F-florbetapir PET in a Phase 2/3 clinical trial of anti-amyloid-ß monoclonal antibodies in dominantly inherited Alzheimer's disease.

PURPOSE: Pittsburgh Compound-B (11C-PiB) and 18F-florbetapir are amyloid-ß (Aß) positron emission tomography (PET) radiotracers that have been used as endpoints in Alzheimer's disease (AD) clinical trials to evaluate the efficacy of anti-Aß monoclonal antibodies. However, comparing drug effects between and within trials may become complicated if different Aß radiotracers were used. To study the consequences of using different Aß radiotracers to measure Aß clearance, we performed a head-to-head comparison of 11C-PiB and 18F-florbetapir in a Phase 2/3 clinical trial of anti-Aß monoclonal antibodies. METHODS: Sixty-six mutation-positive participants enrolled in the gantenerumab and placebo arms of the first Dominantly Inherited Alzheimer Network Trials Unit clinical trial (DIAN-TU-001) underwent both 11C-PiB and 18F-florbetapir PET imaging at baseline and during at least one follow-up visit. For each PET scan, regional standardized uptake value ratios (SUVRs), regional Centiloids, a global cortical SUVR, and a global cortical Centiloid value were calculated. Longitudinal changes in SUVRs and Centiloids were estimated using linear mixed models. Differences in longitudinal change between PET radiotracers and between drug arms were estimated using paired and Welch two sample t-tests, respectively. Simulated clinical trials were conducted to evaluate the consequences of some research sites using 11C-PiB while other sites use 18F-florbetapir for Aß PET imaging. RESULTS: In the placebo arm, the absolute rate of longitudinal change measured by global cortical 11C-PiB SUVRs did not differ from that of global cortical 18F-florbetapir SUVRs. In the gantenerumab arm, global cortical 11C-PiB SUVRs decreased more rapidly than global cortical 18F-florbetapir SUVRs. Drug effects were statistically significant across both Aß radiotracers. In contrast, the rates of longitudinal change measured in global cortical Centiloids did not differ between Aß radiotracers in either the placebo or gantenerumab arms, and drug effects remained statistically significant. Regional analyses largely recapitulated these global cortical analyses. Across simulated clinical trials, type I error was higher in trials where both Aß radiotracers were used versus trials where only one Aß radiotracer was used. Power was lower in trials where 18F-florbetapir was primarily used versus trials where 11C-PiB was primarily used. CONCLUSION: Gantenerumab treatment induces longitudinal changes in Aß PET, and the absolute rates of these longitudinal changes differ significantly between Aß radiotracers. These differences were not seen in the placebo arm, suggesting that Aß-clearing treatments may pose unique challenges when attempting to compare longitudinal results across different Aß radiotracers. Our results suggest converting Aß PET SUVR measurements to Centiloids (both globally and regionally) can harmonize these differences without losing sensitivity to drug effects. Nonetheless, until consensus is achieved on how to harmonize drug effects across radiotracers, and since using multiple radiotracers in the same trial may increase type I error, multisite studies should consider potential variability due to different radiotracers when interpreting Aß PET biomarker data and, if feasible, use a single radiotracer for the best results. TRIAL REGISTRATION: ClinicalTrials.gov NCT01760005. Registered 31 December 2012. Retrospectively registered.

Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis.

BACKGROUND: Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim to address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, a comprehensive benchmarking of such approaches has not yet been reported. Here we benchmarked four causal reasoning algorithms (SigNet, CausalR, CausalR ScanR and CARNIVAL) with four networks (the smaller Omnipath network vs. 3 larger MetaBase™ networks), using LINCS L1000 and CMap microarray data, and assessed to what extent each factor dictated the successful recovery of direct targets and compound-associated signalling pathways in a benchmark dataset comprising 269 compounds. We additionally examined impact on performance in terms of the functions and roles of protein targets and their connectivity bias in the prior knowledge networks. RESULTS: According to statistical analysis (negative binomial model), the combination of algorithm and network most significantly dictated the performance of causal reasoning algorithms, with the SigNet recovering the greatest number of direct targets. With respect to the recovery of signalling pathways, CARNIVAL with the Omnipath network was able to recover the most informative pathways containing compound targets, based on the Reactome pathway hierarchy. Additionally, CARNIVAL, SigNet and CausalR ScanR all outperformed baseline gene expression pathway enrichment results. We found no significant difference in performance between L1000 data or microarray data, even when limited to just 978 'landmark' genes. Notably, all causal reasoning algorithms also outperformed pathway recovery based on input DEGs, despite these often being used for pathway enrichment. Causal reasoning methods performance was somewhat correlated with connectivity and biological role of the targets. CONCLUSIONS: Overall, we conclude that causal reasoning performs well at recovering signalling proteins related to compound MoA upstream from gene expression changes by leveraging prior knowledge networks, and that the choice of network and algorithm has a profound impact on the performance of causal reasoning algorithms. Based on the analyses presented here this is true for both microarray-based gene expression data as well as those based on the L1000 platform.

Combining tau-PET and fMRI meta-analyses for patient-centered prediction of cognitive decline in Alzheimer's disease.

BACKGROUND: Tau-PET is a prognostic marker for cognitive decline in Alzheimer's disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for determining patient-specific cognitive endpoints in clinical trials. Here, we aimed to examine whether tau-PET in cognitive-domain-specific brain regions, identified via fMRI meta-analyses, allows the prediction of domain-specific cognitive decline. Further, we aimed to determine whether tau-PET-informed personalized cognitive composites capture patient-specific cognitive trajectories more sensitively than conventional cognitive measures. METHODS: We included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants classified as controls (i.e., amyloid-negative, cognitively normal, n = 121) or Alzheimer's disease-spectrum (i.e., amyloid-positive, cognitively normal to dementia, n = 140), plus 111 AVID-1451-A05 participants for independent validation (controls/Alzheimer's disease-spectrum=46/65). All participants underwent baseline 18F-flortaucipir tau-PET, amyloid-PET, and longitudinal cognitive testing to assess annual cognitive changes (i.e., episodic memory, language, executive functioning, visuospatial). Cognitive changes were calculated using linear mixed models. Independent meta-analytical task-fMRI activation maps for each included cognitive domain were obtained from the Neurosynth database and applied to tau-PET to determine tau-PET signal in cognitive-domain-specific brain regions. In bootstrapped linear regression, we assessed the strength of the relationship (i.e., partial R2) between cognitive-domain-specific tau-PET vs. global or temporal-lobe tau-PET and cognitive changes. Further, we used tau-PET-based prediction of domain-specific decline to compose personalized cognitive composites that were tailored to capture patient-specific cognitive decline. RESULTS: In both amyloid-positive cohorts (ADNI [age = 75.99±7.69] and A05 [age = 74.03±9.03]), cognitive-domain-specific tau-PET outperformed global and temporal-lobe tau-PET for predicting future cognitive decline in episodic memory, language, executive functioning, and visuospatial abilities. Further, a tau-PET-informed personalized cognitive composite across cognitive domains enhanced the sensitivity to assess cognitive decline in amyloid-positive subjects, yielding lower sample sizes required for detecting simulated intervention effects compared to conventional cognitive endpoints (i.e., memory composite, global cognitive composite). However, the latter effect was less strong in A05 compared to the ADNI cohort. CONCLUSION: Combining tau-PET with task-fMRI-derived maps of major cognitive domains facilitates the prediction of domain-specific cognitive decline. This approach may help to increase the sensitivity to detect Alzheimer's disease-related cognitive decline and to determine personalized cognitive endpoints in clinical trials.

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.

The incidence of shoulder arthroplasty: rise and future projections compared with hip and knee arthroplasty.

BACKGROUND: There remains a paucity of epidemiologic data from recent years on the incidence of shoulder arthroplasty. We aimed to examine the recent trends and predict future projections of hemiarthroplasty (HA), anatomic (aTSA), and reverse shoulder arthroplasty (RSA), as well as compare these predictions to those for total hip (THA) and knee arthroplasty (TKA). METHODS: The National Inpatient Sample was queried from 2011 to 2017 for HA, aTSA, and RSA, as well as TKA and THA. Linear and Poisson regression was performed to project annual procedural incidence and volume to the year 2025. RESULTS: Between 2011 and 2017, the number of primary shoulder arthroplasties increased by 103.7%. In particular, RSA increased by 191.3%, with 63,845 RSAs performed in 2017. All projection models demonstrated significant increases in shoulder arthroplasty volume and incidence from 2017 to 2025. By 2025, the linear model predicts that shoulder arthroplasty volume will increase by 67.2% to 174,810 procedures whereas the Poisson model predicts a 235.2% increase, to 350,558 procedures by 2025. These growth rate projections outpace those of THA and TKA. CONCLUSIONS: The number of shoulder arthroplasties has been increasing in recent years, largely because of the exponential increases in RSA. The overall incidence is increasing at a greater rate than TKA or THA, with projections continuing to rise over the next decade. These data and projections can be used by policy makers and hospitals to drive initiatives aimed at meeting these projected future demands.

Impact of Do-Not-Resuscitate Orders on Nursing Clinical Decision Making.

OBJECTIVES: Code status specifies the action that healthcare providers should take in the event of cardiac arrest. Studies have shown, however, that patients with do-not-resuscitate/do-not-intubate (DNR/DNI) orders have worse outcomes and do not consistently receive the standard of care. Several studies have demonstrated that physicians behave differently toward patients with DNR/DNI orders, but little research exists into whether DNR/DNI status affects the practice of other members of the care team. Our objective was to determine whether code status affects decision making by nursing staff. METHODS: This was an anonymous, self-administered survey of nurses between April 2018 and March 2019 using SurveyMonkey. The survey contained four previously published clinical vignettes followed by a series of questions regarding specific interventions tailored to reflect nursing escalation of care. Our focus was two local hospitals: one large academic quaternary-referral center and one large community hospital. Registered nurses on medical-surgical units identified based on available unit-specific e-mail listservs from both hospitals were the participants. Nurses in higher-acuity units were excluded. RESULTS: Nurses are significantly less likely to call rapid response or a physician when a patient undergoes certain changes in clinical status if the patient is labeled as DNR/DNI rather than full code. For all of the vignettes, respondents were less likely to say they would call rapid response or a physician for patients with a DNR/DNI status who developed tachycardia (P < 0.001). Nurses also were less likely to escalate care for patients with DNR/DNI status who developed tachypnea or mental status changes. Nurses were equally likely to call a physician for the development of abdominal pain or new hypotension (P > 0.05). Nurses with >3 years of experience were less likely to escalate care throughout the vignettes (odds ratio <1). CONCLUSIONS: This study is the first to demonstrate that code status affects decision making by nursing staff. It highlights the limitations that code status designations create with regard to patient care. By acknowledging that patients with DNR/DNI orders receive different care, we can create systems in which patients are treated equally, regardless of their code status.

A difference degree test for comparing brain networks.

Recently, there has been a proliferation of methods investigating functional connectivity as a biomarker for mental disorders. Typical approaches include massive univariate testing at each edge or comparisons of network metrics to identify differing topological features. Limitations of these methods include low statistical power due to the large number of comparisons and difficulty attributing overall differences in networks to local variation. We propose a method to capture the difference degree, which is the number of edges incident to each region in the difference network. Our difference degree test (DDT) is a two-step procedure for identifying brain regions incident to a significant number of differentially weighted edges (DWEs). First, we select a data-adaptive threshold which identifies the DWEs followed by a statistical test for the number of DWEs incident to each brain region. We achieve this by generating an appropriate set of null networks which are matched on the first and second moments of the observed difference network using the Hirschberger-Qi-Steuer algorithm. This formulation permits separation of the network's true topology from the nuisance topology induced by the correlation measure that alters interregional connectivity in ways unrelated to brain function. In simulations, the proposed approach outperforms competing methods in detecting differentially connected regions of interest. Application of DDT to a major depressive disorder dataset leads to the identification of brain regions in the default mode network commonly implicated in this ruminative disorder.

Integrative Bayesian analysis of brain functional networks incorporating anatomical knowledge.

Recently, there has been increased interest in fusing multimodal imaging to better understand brain organization by integrating information on both brain structure and function. In particular, incorporating anatomical knowledge leads to desirable outcomes such as increased accuracy in brain network estimates and greater reproducibility of topological features across scanning sessions. Despite the clear advantages, major challenges persist in integrative analyses including an incomplete understanding of the structure-function relationship and inaccuracies in mapping anatomical structures due to inherent deficiencies in existing imaging technology. This calls for the development of advanced network modeling tools that appropriately incorporate anatomical structure in constructing brain functional networks. We propose a hierarchical Bayesian Gaussian graphical modeling approach which models the brain functional networks via sparse precision matrices whose degree of edge specific shrinkage is a random variable that is modeled using both anatomical structure and an independent baseline component. The proposed approach adaptively shrinks functional connections and flexibly identifies functional connections supported by structural connectivity knowledge. This enables robust brain network estimation even in the presence of misspecified anatomical knowledge, while accommodating heterogeneity in the structure-function relationship. We implement the approach via an efficient optimization algorithm which yields maximum a posteriori estimates. Extensive numerical studies involving multiple functional network structures reveal the clear advantages of the proposed approach over competing methods in accurately estimating brain functional connectivity, even when the anatomical knowledge is misspecified up to a certain degree. An application of the approach to data from the Philadelphia Neurodevelopmental Cohort (PNC) study reveals gender based connectivity differences across multiple age groups, and higher reproducibility in the estimation of network metrics compared to alternative methods.