Biology subjects

Gaser, C.

Publications and source records attributed to Gaser, C..

14 recordsLinked to original sources

bioRxiv2026

Distributed Genetic Effects on Human Brain Structure Emerge Across Multiple Spatial Scales

Genome-wide association studies (GWAS) have identified hundreds of common genetic variants associated with regional brain volumes, enabling the construction of polygenic scores (PGS) that summarize genetic predisposition for variation in specific neuroanatomical traits. To investigate how these genetic influences are exerted spatially throughout the brain, we computed PGS for ten brain volume phenotypes, including nine major subcortical structures and intracranial volume. Each locus was weighted by its estimated GWAS effect size on regional volume in the original GWAS. In an independent, non-overlapping sample of 2,830 UK Biobank participants, we performed whole-brain voxel-based morphometry (VBM) analyses of 3D volumetric brain MRI to reveal voxel-wise associations between each PGS and modulated gray matter volume (GMV). To probe genetic effects across multiple spatial scales, analyses were repeated across Gaussian smoothing kernels ranging from 2-mm to 12-mm full-width at half-maximum (FWHM). Several PGS demonstrated highly significant associations with GMV, including localized effects in the hippocampus, amygdala, thalamus, and basal ganglia, whereas the brainstem PGS showed more widespread associations throughout the brain. For most of the PGS, the fraction of voxels surviving the false discovery rate (FDR) correction increased with increasing FWHM. Peak voxel-wise significance was often strongest at intermediate smoothing levels. Hippocampal significance maps showed progressively larger regions of significant signal at higher smoothing levels, and subsampling showed that detectable signal remained present even with substantial reductions in sample size. These findings suggest that genetic influences on brain morphology are expressed across multiple spatial scales, with consequences that may help to guide the design of deep learning methods to discover genomic loci associated with brain structure and brain diseases.

neuroscience↗

bioRxiv2026

Mapping Focal and Generalized Effects of Common Genetic Variants on Human Brain Structure

Genome-wide association studies (GWAS) have advanced the quest to understand how specific genetic variants influence human brain structure and function. Recent work has identified hundreds of common variants associated with subcortical brain volumes, sparking interest in how these genetic markers overlap across brain networks. While this can be estimated by hierarchical clustering of the genetic correlation matrix to identify modular patterns of shared architecture, no brain-wide maps of these effects are available. To address this, we computed polygenic scores (PGS) from loci associated with ten brain volume regions of interest (ROIs): nine major subcortical structures and intracranial volume, with each locus weighted by its association with regional volume. In an independent sample from the discovery GWAS, we performed large-scale segmentation of 3D volumetric T1-weighted MRI scans using voxel-based morphometry (VBM) to map 3D profile of regions where gray matter volume (GMV) was associated with each PGS. We found statistically significant, localized effects for PGS defined for the amygdala, thalamus, and basal ganglia, but PGS for brainstem volume was associated with widespread differences throughout the brain. These brain-wide maps reveal patterns consistent with both localized and distributed genetic influences, offering a novel approach to interpret the genomic architecture of brain structure.

neuroscience↗

bioRxiv2026

ComCat: Combating Covariate Effects in Brain Analysis

As neuroimaging analysis shifts toward large-scale, multi-site studies, managing the unwanted variability introduced by combining heterogeneous datasets has become a critical challenge. Although tools such as ComBat and its neuroimaging extensions are widely used to address this variability, they only permit the modeling of categorical site effects and cannot account for continuous sources of confounding, such as image quality, head motion, and acquisition parameters. We introduce ComCat, an extension of the ComBat framework that preserves biologically relevant covariates while removing the effects of categorical site indicators and continuous nuisance variables. The latter are modeled as smooth nonlinear functions via B-spline basis expansion. ComCat is applicable to a broad range of brain analysis tasks, including voxel- and surface-based morphometry, normative modeling, and machine learning-based prediction. To demonstrate its capabilities, we evaluated ComCat on brain age prediction across five datasets covering complementary multi-site harmonization scenarios: ON-Harmony (10 subjects x 6 scanners; n = 80); the Buchert traveling-phantom dataset (1 subject x 116 scanners; n = 531); the Tohoku single-scanner, varying-acquisition dataset (n = 121); MR-ART (148 subjects with varying motion levels); and an ABIDE subset comprising 229 control subjects and 208 individuals with autism spectrum disorder across 14 scanners. Using image quality measures derived from CAT12 as continuous nuisance variables, ComCat reduced the mean absolute error (MAE) in brain age prediction relative to ComBat-GAM in all five datasets, including the two scenarios where site information was unavailable or uninformative. In the ABIDE dataset, ComCat improved harmonization while preserving the difference between the control and ASD groups, demonstrating that scanner-related variance can be removed without affecting biologically meaningful signals. ComCat can operate with or without site labels and is agnostic to the source of image quality metrics.

neuroscience↗

bioRxiv2026

A normative reference for large-scale human brain dynamics across the lifespan

Human brain function emerges from dynamic reconfigurations of large-scale neural networks. While population-level reference charts have transformed the study of static brain structure and connectivity, an equivalent normative framework for intrinsic brain dynamics has been lacking. This gap has limited our ability to characterize individual variability, development, ageing, and mental health conditions at scale. Here, we establish a population-level normative reference for large-scale human brain dynamics using resting-state fMRI data from more than 10,000 individuals spanning the lifespan and 91 scanning sites. We derive a compact set of recurring brain-state configurations that are reproducible across scanners and acquisition paradigms and that generalize to previously unseen cohorts. Anchoring these dynamic states to normative lifespan models enables the quantification of individual deviations relative to population reference distributions. We show that intrinsic brain dynamics undergo systematic reorganization across development and ageing, with pronounced changes before early adulthood and more gradual modulation thereafter. Applying this framework across multiple mental health conditions reveals disorder-specific and highly heterogeneous deviations in brain dynamics that are not captured by static neuroimaging measures. Robust transfer to independent cohorts and longitudinal analyses demonstrate that normative brain dynamics can be reliably assessed out of distribution. These results delineate a population-scale dynamic architecture of the human brain and extend normative brain mapping from static phenotypes to the temporal domain, providing a reference framework for studying brain function across the lifespan in health and disease.

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bioRxiv2025

The Good, the Bad, and the Ugly: Segmentation-Based Quality Control of Structural Magnetic Resonance Images

The processing and analysis of magnetic resonance images is highly dependent on the quality of the input data, and systematic differences in quality can consequently lead to loss of sensitivity or biased results. However, varying image properties due to different scanners and acquisition protocols, as well as subject-specific image interferences, such as motion artifacts, can be incorporated in the analysis. A reliable assessment of image quality is therefore essential to identify critical outliers that may bias results. Here we present a quality assessment for structural (T1-weighted) images using tissue classification. We introduce multiple useful image quality measures, standardize them into quality scales and combine them into an integrated structural image quality rating to facilitate the interpretation and fast identification of outliers with (motion) artifacts. The reliability and robustness of the measures are evaluated using synthetic and real datasets. Our study results demonstrate that the proposed measures are robust to simulated segmentation problems and variables of interest such as cortical atrophy, age, sex, brain size and severe disease-related changes, and might facilitate the separation of motion artifacts based on within-protocol deviations. The quality control framework presents a simple but powerful tool for the use in research and clinical settings.

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bioRxiv2024

Harnessing early multimodal motor training to drive motor recovery and brain-wide structural reorganization after stroke

Stroke survivors often suffer from severe motor impairments, highlighting the critical need for effective rehabilitation strategies. In experimental models, extensive task-specific motor training within the first few weeks post-stroke significantly mitigates motor deficits. However, whether more multimodal motor training approaches after stroke can restore task-specific performance in non-trained motor tasks remains largely unknown. Additionally, while stroke itself triggers structural brain reorganization, the influence of early multimodal motor training on this process remains unclear. Here, we employed T2-weighted MRI to investigate brain region-specific volumetric changes over eight weeks in rats subjected to stroke and subsequent early multimodal motor training. We found that this combination not only facilitated task-specific motor function recovery, but also induced dramatic, multi-region, brain-wide volumetric changes. Specifically, over 80 locations within 50 distinct brain regions across both hemispheres exhibited substantial volumetric alterations in a predominantly bilateral symmetric pattern. In contrast, stroke or training alone resulted in changes in 8-15 locations within 7-13 brain regions, with stroke alone primarily affecting the infarcted hemisphere. Analysis of temporal volume changes revealed two distinct trajectories in post-stroke trained rats: one of initial swelling followed by shrinkage, and another of initial shrinkage followed by swelling, suggesting an early and delayed motor learning period. Overall, our findings demonstrate that multimodal motor training early after stroke effectively restores task-specific motor function and profoundly reshapes brain structure on a brain-wide scale, offering vital insights for developing optimized rehabilitation protocols to maximize recovery in stroke patients.

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bioRxiv2024

Approximation of bone mineral density and subcutaneous adiposity using T1-weighted images of the human head

Bones and brain are intricately connected and scientific interest in their interaction is growing. This has become particularly evident in the framework of clinical applications for various medical conditions, such as obesity and osteoporosis. The adverse effects of obesity on brain health have long been recognised, but few brain imaging studies provide sophisticated body composition measures. Here we propose to extract the following bone- and adiposity-related measures from T1-weighted MR images of the head: an approximation of skull bone mineral density (BMD), skull bone thickness, and two approximations of subcutaneous fat (i.e., the intensity and thickness of soft non-brain head tissue). The measures pertaining to skull BMD, skull bone thickness, and intensi-ty-based adiposity proxy proved to be reliable (r=.93/.83/.74, p<.001) and valid, with high correlations to DXA-de-rived head BMD values (rho=.70, p<.001) and MRI-derived abdominal subcutaneous adipose volume (rho=.62, p<.001). Thickness-based adiposity proxy had only a low retest reliability (r=.58, p<.001).The outcomes of this study constitute an important step towards extracting relevant non-brain features from available brain scans.

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bioRxiv2023

Cross-validation for the estimation of effect size generalizability in mass-univariate brain-wide association studies

IntroductionStatistical effect sizes are systematically overestimated in small samples, leading to poor generalizability and replicability of findings in all areas of research. Due to the large number of variables, this is particularly problematic in neuroimaging research. While cross-validation is frequently used in multivariate machine learning approaches to assess model generalizability and replicability, the benefits for mass-univariate brain analysis are yet unclear. We investigated the impact of cross-validation on effect size estimation in univariate voxel-based brain-wide associations, using body mass index (BMI) as an exemplary predictor. MethodsA total of n=3401 adults were pooled from three independent cohorts. Brain-wide associations between BMI and gray matter structure were tested using a standard linear mass-univariate voxel-based approach. First, a traditional non-cross-validated analysis was conducted to identify brain-wide effect sizes in the total sample (as an estimate of a realistic reference effect size). The impact of sample size (bootstrapped samples ranging from n=25 to n=3401) and cross-validation on effect size estimates was investigated across selected voxels with differing underlying effect sizes (including the brain-wide lowest effect size). Linear effects were estimated within training sets and then applied to unseen test set data, using 5-fold cross-validation. Resulting effect sizes (explained variance) were investigated. ResultsAnalysis in the total sample (n=3401) without cross-validation yielded mainly negative correlations between BMI and gray matter density with a maximum effect size of R2p=.036 (peak voxel in the cerebellum). Effects were overestimated exponentially with decreasing sample size, with effect sizes up to R2p=.535 in samples of n=25 for the voxel with the brain-wide largest effect and up to R2p=.429 for the voxel with the brain-wide smallest effect. When applying cross-validation, linear effects estimated in small samples did not generalize to an independent test set. For the largest brain-wide effect a minimum sample size of n=100 was required to start generalizing (explained variance >0 in unseen data), while n=400 were needed for smaller effects of R2p =.005 to generalize. For a voxel with an underlying null effect, linear effects found in non-cross-validated samples did not generalize to test sets even with the maximum sample size of n=3401. Effect size estimates obtained with and without cross-validation approached convergence in large samples. DiscussionCross-validation is a useful method to counteract the overestimation of effect size particularly in small samples and to assess the generalizability of effects. Train and test set effect sizes converge in large samples which likely reflects a good generalizability for models in such samples. While linear effects start generalizing to unseen data in samples of n>100 for large effect sizes, the generalization of smaller effects requires larger samples (n>400). Cross-validation should be applied in voxel-based mass-univariate analysis to foster accurate effect size estimation and improve replicability of neuroimaging findings. We provide open-source python code for this purpose (https://osf.io/cy7fp/?view_only=a10fd0ee7b914f50820b5265f65f0cdb).

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bioRxiv2022

BrainAGE revisited: Keeping up the pace with deep learning methods

Since the introduction of the BrainAGE method (Franke et al., 2010), novel machine learning methods of brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its interpretation and its potential use as a biomarker of brain health. We revised our previous BrainAGE approach, originally utilising relevance vector regression (RVR), and substituted it with Gaussian process regression (GPR), which enables more stable processing of larger datasets, such as the UK Biobank (UKB). In addition, we extended the global BrainAGE approach to regional BrainAGE, providing spatially specific scores for 5 brain lobes per hemisphere. We tested the performance of the new algorithms under several different conditions and investigated their validity on the ADNI and schizophrenia samples, as well as on a synthetic dataset of neocortical thinning. The results show an improved performance of the reframed global model on the UKB sample with a mean absolute error (MAE) of less than 2 years and a significant difference in BrainAGE between healthy participants and patients with Alzheimers disease and schizophrenia. Moreover, the workings of the algorithm show meaningful effects for a simulated neocortical atrophy dataset. The regional BrainAGE model performed well on two clinical samples, showing disease-specific patterns for different levels of impairment. The results demonstrate that the new improved algorithms provide reliable and valid brain age estimations.

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bioRxiv2022

CAT - A Computational Anatomy Toolbox for the Analysis of Structural MRI Data

A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT) - a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface, but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams - illustrated on an example dataset - allow for voxel-based, surface-based, as well as region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT, while offering a citable standard for the neuroscience community.

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bioRxiv2022

To smooth or not to smooth: One step closer to single-voxel accuracy without spatial smoothing

Traditionally, when conducting voxel- or vertex-wise analyses in neuroimaging studies, it seemed imperative that brain data are convoluted with a Gaussian kernel, a procedure known as "spatial smoothing". However, we suggest that - under certain conditions - smoothing may be omitted for the benefit of an improved regional specificity. We demonstrate the suitability of this omission by combining high-dimensional spatial registration and threshold-free cluster enhancement (TFCE) in a sample of 754 brains. Our findings revealed that, without smoothing, it is possible to capture brain atrophy within the hippocampal complex while dissociating neighboring areas (cornu ammonis, dentate gyrys, subiculum, and amygdala). In contrast, the traditional smoothing step would result in a single hippocampal cluster (the larger the smoothing kernel, the lower the specificity). Supplemental analyses not only varying the size of the smoothing kernel, but also the size of the sample, the signal-to-noise ratio, as well as the accuracy of the spatial registration confirm that no smoothing (or less smoothing) leads to increased specificity while maintaining sensitivity, at least for small-scale structures (e.g., hippocampus and amygdala). Nevertheless, classic analyses based on smoothed data will continue to provide important insights, especially for large-scale structures (e.g., cortical regions).

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bioRxiv2020

Experience-dependent structural plasticity in the adult brain: How the learning brain grows.

Volumetric magnetic resonance imaging studies have shown that intense learning can be associated with grey matter volume increases in the adult brain. The underlying mechanisms are poorly understood. Here we used monocular deprivation in rats to analyze the mechanisms underlying use-dependent grey matter increases. Optometry for quantification of visual acuity was combined with volumetric magnetic resonance imaging and microscopic techniques in longitudinal and cross-sectional studies. We found an increased spatial vision of the open eye which was associated with a transient increase in the volumes of the contralateral visual and lateral entorhinal cortex. In these brain areas dendrites of neurons elongated, and there was a strong increase in the number of spines, the targets of synapses, which was followed by spine maturation and partial pruning. Astrocytes displayed a transient pronounced swelling and underwent a reorganization of their processes. The use-dependent increase in grey matter corresponded predominantly to the swelling of the astrocytes. Experience-dependent increase in brain grey matter volume indicates a gain of structure plasticity with both synaptic and astrocyte remodeling. HighlightsO_LIPerception learning causes a transient increase in brain grey matter volume detectable by MRI. C_LIO_LIThis learning results in pronounced changes of neuronal dendrites and an increase in the number of dendritic spines. C_LIO_LIStructural neuronal plasticity is associated with a reorganization and transient swelling of astrocytes. C_LIO_LIBrain volume and astrocyte volume return to baseline post-learning, with a persistent increase in the number of mature spines. C_LI

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bioRxiv2020

Chimpanzee Brain Morphometry Utilizing Standardized MRI Preprocessing and Macroanatomical Annotations

Chimpanzees are among the closest living relatives to humans and, as such, provide a crucial comparative model for investigating primate brain evolution. In recent years, human brain mapping has strongly benefited from enhanced computational models and image processing pipelines that could also improve data analyses in animals by using species-specific templates. In this study, we use structural MRI data from the National Chimpanzee Brain Resource (NCBR) to develop the chimpanzee brain reference template Juna.Chimp for spatial registration and the macro-anatomical brain parcellation Davi130 for standardized whole-brain analysis. Additionally, we introduce a ready-to-use image processing pipeline built upon the CAT12 toolbox in SPM12, implementing a standard human image preprocessing framework in chimpanzees. Applying this approach to data from 178 subjects, we find strong evidence for age-related GM atrophy in multiple regions of the chimpanzee brain, as well as, a human-like anterior-posterior pattern of hemi-spheric asymmetry in medial chimpanzee brain regions.

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bioRxiv2020

The missing role of gray matter in studying brain controllability

1Brain controllability properties are normally derived from the white matter fiber tracts in which the neural substrate of the actual energy consumption, namely the gray matter, has been widely ignored. Here, we study the relationship between gray matter volume of regions across the whole cortex and their respective control property derived from the structural architecture of the white matter fiber tracts. The data suggests that the ability of white fiber tracts to exhibit control at specific nodes not only depends on the connection strength of the structural connectome but additionally strongly depends on gray matter volume at the host nodes. Our data indicates that connectivity strength and gray matter volume interact with respect to the brains control properties, such that gray matter exerts the great impact in regions with high structural connectivity. Disentangling effects of the regional gray matter volume and connectivity strength, we found that frontal and sensory areas play crucial roles in controllability. Together these results suggest that structural and regional properties of the white matter and gray matter provide complementary information in studying the control properties of the intrinsic structural and functional architectural of the brain.

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