Biology subjects

Ganz, M.

Publications and source records attributed to Ganz, M..

10 recordsLinked to original sources

bioRxiv2026

Neurostructural correlates of real-life social drinking risk in alcohol use disorder

The social context of alcohol consumption is increasingly recognized as an important factor in the development of alcohol use disorder (AUD). However, individuals with AUD may differ in how strongly alcohol use by people around them is associated with their own drinking, and the neurostructural basis of this variation remains unclear. Here we studied 83 individuals with AUD (53 men, 30 women; mean [SD] age, 37.20 [12.42] years; median DSM-5 AUD criteria, 4) using six weeks of ecological momentary assessment and structural magnetic resonance imaging (n = 80). Across 5,221 non-solitary observations, greater exposure to surrounding drinkers was associated with increased odds of momentary drinking (odds ratio, 4.23; 95% CI, 3.60-4.97), with significant between-person variation that we quantified as social drinking risk (SDR). Higher SDR was associated with lower gray matter volume in the pregenual anterior cingulate cortex (T = 5.40; P < .001) and weaker pregenual anterior cingulate cortex-ventral striatum structural covariance (T = 3.15; P = .032). These findings support SDR as an ecologically grounded phenotype linking daily-life social-context sensitivity to cingulostriatal structure, with potential relevance for mechanistic and interventional research in AUD.

neuroscience↗

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↗

bioRxiv2026

Psilocybin acutely reduces low-frequency BOLD power and frequency-specific connectivity

Psilocybin and other serotonergic drugs acutely alter human brain function and large-scale connectivity as measured with BOLD fMRI, but whether these effects are frequency-specific remains unknown. We applied multitaper spectral and cross-spectral analyses to resting-state fMRI data from 28 healthy volunteers scanned multiple times acutely following oral psilocybin administration (0.2 - 0.3 mg/kg), together with plasma psilocin measurements, to estimate psilocin associations with temporal frequency-specific activity and connectivity. Psilocybin produced a selective reduction in low-frequency spectral power (0.01 - 0.06Hz) and an increase in spectral entropy, with the strongest effects in transmodal networks. We also observed a reduction in low-frequency connectivity energy explained by the unimodal/transmodal axis. These findings demonstrate that psilocin induces spatially distributed, frequency-dependent alterations, suggesting that broadband fMRI analyses may obscure low-frequency dynamics. Frequency-resolved approaches may offer greater sensitivity for characterizing psychedelic effects on brain activity.

neuroscience↗

bioRxiv2025

Inter-individual variability of neurotransmitter receptor and transporter density in the human brain

Neurotransmitter receptors guide the propagation of signals between brain regions. Mapping receptor distributions in the brain is therefore necessary for understanding how neurotransmitter systems mediate the link between brain structure and function. Normative receptor density can be estimated using group averages from Positron Emission Tomography (PET) imaging. However, the generalizability and reliability of group-average receptor maps depends on the inter-individual variability of receptor density, which is currently unknown. Here we collect group standard deviation brain maps of PET-estimated protein abundance for 12 different neurotransmitter receptors and transporters across 7 neurotransmitter systems, including dopamine, serotonin, acetylcholine, glutamate, GABA, cannabinoid, and opioid. We illustrate how cortical and subcortical inter-individual variability of receptor and transporter density varies across brain regions and across neurotransmitter systems. We complement inter-individual variability with inter-regional variability, and show that receptors that vary more across brain regions than across individuals also demonstrate greater out-of-sample spatial consistency. Altogether, this work quantifies how receptor systems vary in healthy individuals, and provides a means of assessing the generalizability of PET-derived receptor density quantification.

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↗

bioRxiv2024

Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET derived features

To better assess the pathology of neurodegenerative disorders and the efficacy of neuroprotective interventions, it is necessary to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A receptors (5-HT2AR) show a particularly profound age-related decline and are also reduced in neurodegenerative disorders, such as Alzheimers disease. This study investigates whether the decline in 5-HT2AR binding, measured in vivo using positron emission tomography (PET), can be used as a biomarker for brain aging. Specifically, we aim to 1) predict brain age using 5-HT2AR binding outcomes, 2) compare 5-HT2AR-based predictions of brain age to predictions based on gray matter (GM) volume, as determined with structural magnetic resonance imaging (MRI), and 3) investigate whether combining 5-HT2AR and GM volume data improves prediction. We used PET and MR images from 209 healthy individuals aged between 18 and 85 years (mean=38, std=18), and estimated 5-HT2AR binding and GM volume for 14 cortical and subcortical regions. Different machine learning algorithms were applied to predict chronological age based on 5-HT2AR binding, GM volume, and the combined measures. The mean absolute error (MAE) and a cross-validation approach were used for evaluation and model comparison. We find that both the cerebral 5-HT2AR binding (mean MAE=6.63 years, std=0.74 years) and GM volume (mean MAE=6.95 years, std=0.83 years) predict chronological age accurately. Combining the two measures improves the prediction further (mean MAE=5.54 years, std=0.68). In conclusion, 5-HT2AR binding measured using PET might be useful for improving the quantification of a biomarker for brain aging.

neuroscience↗

bioRxiv2023

Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test-retest reliability of publicly available software packages

BackgroundBrain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to assess the biological age of the human brain. The difference between a persons chronological age and the estimated brain age is thought to reflect deviations from a normal aging trajectory, indicating a slower, or accelerated, biological aging process. Several pre-trained software packages for predicting brain age are publicly available. In this study, we perform a head-to-head comparison of such packages with respect to 1) predictive accuracy, 2) test-retest reliability, and 3) the ability to track age progression over time. MethodsWe evaluated the six brain age prediction packages: brainageR, DeepBrainNet, brainage, ENIGMA, pyment, and mccqrnn. The accuracy and test-retest reliability were assessed on MRI data from 372 healthy people aged between 18.4 and 86.2 years (mean 38.7 {+/-} 17.5 years). ResultsAll packages showed significant correlations between predicted brain age and chronological age (r = 0.66 to 0.97, p < 0.001), with pyment displaying the strongest correlation. The mean absolute error was between 3.56 (pyment) and 9.54 years (ENIGMA). brainageR, pyment, and mccqrnn were superior in terms of reliability (ICC values between 0.94 - 0.98), as well as predicting age progression over a longer time span. ConclusionOf the six packages, pyment and brainageR consistently showed the highest accuracy and test-retest reliability.

neuroscience↗

bioRxiv2021

PET-BIDS, an extension to the brain imaging data structure for positron emission tomography

The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets. It serves not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data (PET-BIDS). We describe the PET-BIDS standard in detail and share several open-access datasets curated following PET-BIDS. Additionally, we highlight several tools which are already available for converting, validating and analyzing PET-BIDS datasets.

neuroscience↗

bioRxiv2020

A High-Resolution In Vivo Atlas of the Human Brain's Benzodiazepine Binding Site of GABAA Receptors

Gamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the human brain and plays a key role in several brain functions and neuropsychiatric disorders such as anxiety, epilepsy, and depression. The binding of benzodiazepines to the benzodiazepine receptor sites (BZR) located on GABAA receptors (GABAARs) potentiates the inhibitory effect of GABA leading to the anxiolytic, anticonvulsant and sedative effects used for treatment of those disorders. However, the function of GABAARs and the expression of BZR protein is determined by the GABAAR subunit stoichiometry (19 genes coding for individual subunits), and it remains to be established how the pentamer composition varies between brain regions and individuals. Here, we present a quantitative high-resolution in vivo atlas of the human brain BZRs, generated on the basis of [11C]flumazenil Positron Emission Tomography (PET) data. Next, based on autoradiography data, we transform the PET-generated atlas from binding values into BZR protein density. Finally, we examine the brain regional association with mRNA expression for the 19 subunits in the GABAAR, including an estimation of the minimally required expression of mRNA levels for each subunit to translate into BZR protein. This represents the first publicly available quantitative high-resolution in vivo atlas of the spatial distribution of BZR densities in the healthy human brain. The atlas provides a unique neuroscientific tool as well as novel insights into the association between mRNA expression for individual subunits in the GABAAR and the BZR density at each location in the brain.

neuroscience↗

bioRxiv2019

The oncoprotein DEK affects the outcome of PARP1/2 inhibition during replication stress

DNA replication stress is a major source of genomic instability and is closely linked to tumor formation and progression. Poly(ADP-ribose)polymerases1/2 (PARP1/2) enzymes are activated in response to replication stress resulting in poly(ADP-ribose) (PAR) synthesis. PARylation plays an important role in the remodelling and repair of impaired replication forks, providing a rationale for targeting highly replicative cancer cells with PARP1/2 inhibitors. The human oncoprotein DEK is a unique, non-histone chromatin architectural protein whose deregulated expression is associated with the development of a wide variety of human cancers. Recently, we showed that DEK is a high-affinity target of PARylation and that it promotes the progression of impaired replication forks. Here, we investigated a potential functional link between PAR and DEK in the context of replication stress. Under conditions of mild replication stress induced either by topoisomerase1 inhibition with camptothecin or nucleotide depletion by hydroxyurea, we found that the effect of acute PARP1/2 inhibition on replication fork progression is dependent on DEK expression. Reducing DEK protein levels also overcomes the restart impairment of stalled forks provoked by blocking PARylation. Non-covalent DEK-PAR interaction via the central PAR-binding domain of DEK is crucial for counteracting PARP1/2 inhibition as shown for the formation of RPA positive foci in hydroxyurea treated cells. Finally, we show by iPOND and super resolved microscopy that DEK is not directly associated with the replisome since it binds to DNA at the stage of chromatin formation. Our report sheds new light on the still enigmatic molecular functions of DEK and suggests that DEK expression levels may influence the sensitivity of cancer cells to PARP1/2 inhibitors.

cancer biology↗