Eierud, C. | bioRxiv Science

bioRxiv2026

Building an open ecosystem for molecular neuroimaging: standards and tools from the OpenNeuroPET initiative

Molecular neuroimaging with positron emission tomography (PET) and single-photon emission computed tomography (SPECT) enables quantification of specific molecular targets in the living brain. Despite its scientific impact, molecular neuroimaging research has historically faced challenges due to high costs, small sample sizes, laboratory-specific analysis pipelines, and limited large-scale data sharing. These factors have hindered reproducibility and the broader reuse of valuable PET datasets. The OpenNeuroPET initiative was established to address these barriers by developing standards, infrastructure, and open-source tools currently focused on PET. Through collaborations across Europe and North America, OpenNeuroPET has supported the PET extension of the Brain Imaging Data Structure (PET-BIDS), providing a standardized framework for PET datasets and metadata. Building on PET-BIDS, tools such as PET2BIDS, ezBIDS, and BIDSCoin facilitate data conversion and curation. In parallel, OpenNeuro now hosts PET-BIDS datasets for open sharing, while complementary platforms such as PublicnEUro provide controlled-access pathways designed to support compliance with the European Union General Data Protection Regulation (GDPR). Emerging open-source workflows further support automated, reproducible PET analysis, promoting harmonization across centers. Together, these developments mark an important step toward an open molecular neuroimaging community in which datasets, software, and workflows can be transparently shared, reused, and scaled for collaborative research.

bioinformatics↗

bioRxiv2025

PET-derived amyloid patterns in gray and white matter across Alzheimer's disease: A high-model-order ICA

INTRODUCTIONAlzheimers Disease (AD) is a neurodegenerative disorder marked by gray matter (GM) changes driven by amyloid-beta (A{beta}) plaques and neurofibrillary tangles. While GM alterations are well documented, spatially distinct patterns of homogeneous A{beta} uptake and white matter (WM) involvement remain underexplored. METHODSWe applied high-order independent component analysis (ICA) to 716 [18F]Florbetapir PET scans, identifying 80 GM and 13 WM networks. Diagnostic and cognitive associations were evaluated via statistical modeling. RESULTSIdentified networks delineated a progression trajectory, with mild cognitive impairment (MCI) profiles in temporoparietal and frontal subdomains more closely aligned with AD than cognitively normal (CN) profiles. GM networks, including the hippocampal-entorhinal complex and precuneus, and WM networks, including the retrolenticular internal capsule, demonstrated robust associations with cognitive performance. DISCUSSIONOur findings highlight the utility of high-order ICA in identifying reproducible A{beta} networks and the contribution of WM networks, such as the posterior corpus callosum, in the early pathological landscape of AD.

neuroscience↗

bioRxiv2025

Building Multivariate Molecular Imaging Brain Atlases Using the NeuroMark PET Independent Component Analysis Framework

Molecular imaging analyses using positron emission tomography (PET) data often rely on macro-anatomical regions of interest (ROI), which may not align with chemo-architectural boundaries and obscure functional distinctions. While methods such as independent component analysis (ICA) have been useful to address this limitation, the fully data-driven nature can make it challenging to compare results across studies. Here, we introduce the NeuroMark PET approach, utilizing spatially constrained independent component analysis to define overlapping regions that may reflect the brains molecular architecture. We first generate an ICA template for the PET radiotracer florbetapir (FBP), targeting amyloid-{beta} (A{beta}) accumulation in the brain, using blind ICA on large datasets to identify replicable independent components. Only components that targeted A{beta} were included in this study, defined as A{beta} networks (A{beta}Ns), by omitting components targeting myelin or other non-A{beta} targets. Next, we use the A{beta}Ns as priors for spatially constrained ICA, resulting in a fully automated ICA pipeline called NeuroMark PET. This NeuroMark pipeline, including its A{beta}Ns, was validated against a standard neuroanatomical PET atlas, using data from the Alzheimers Disease Neuroimaging Initiative (ADNI). The study included 296 cognitively normal participants with FBP PET scans and 173 with florbetaben (FBB) PET scans, an analogue radiotracer also targeting A{beta} accumulation. Our results show that NeuroMark PET captures biologically meaningful, participant-specific features, such as subject specific loading values, consistent across individuals, and also shows higher sensitivity and power for detecting age-related changes compared to traditional atlas-based ROIs. Using this framework, we also highlight some of the advantages of using ICA analysis for PET data. In this study, an A{beta}N consists of weighted voxels and forms a pattern throughout the entire brain. For example, components may have weighted values at every voxel and can overlap with one another, enabling the separation of artifacts which may coincide with the A{beta}Ns of interest. In addition, this approach allows for the differentiation, separating white matter components, which may overlap in complex ways with the A{beta}Ns, mainly residing in the neighboring gray matter. Results also showed that the most age associated A{beta}N (representing the cognitive control network, CC1) exhibited a stronger association with age compared with macro-anatomical regions of interest. This may suggest that each NeuroMark FBP A{beta}N represents a spatial network following chemo-architectural uptake with greater biological relevance compared with anatomical ROIs. In summary, the proposed NeuroMark PET approach offers a fully automated framework, providing accurate and reproducible brain A{beta}Ns. This approach enhances our ability to investigate the molecular underpinnings of brain function and pathology, offering an alternative to traditional ROI-based analyses.

neuroscience↗