Biology subjects

Caspers, S.

Publications and source records attributed to Caspers, S..

14 recordsLinked to original sources

bioRxiv2026

Functional and Structural Brain Age Gaps as Complementary Predictive Markers for Cognitive Performance

Cognitive decline is characterized by substantial inter-individual variability across the lifespan, even among clinically healthy adults. However, it is unclear how structural and functional brain organization jointly relate to differences in cognitive performance within the typical populations. We considered a multimodal brain age framework by predicting for each subject its age from either structural MRI or resting-state functional MRI, after training on the 1000BRAINS cohort (901 participants, 18-85 years). Following, a structural and a functional brain age gap markers were defined as the deviations with respect to the real chronological age. We then used the markers to characterize the interplay between structure and function in shaping cognitive performances. Our findings reveal that the functional marker is as important to predict global cognitive performance as the structural marker. The most accurate prediction was found for global cognition, executive functions and episodic memory, followed by working memory, attention and language. In particular, the structural marker was contributing to all domains, except for working memory, whereas the functional marker had the highest contribution. Overall, structural and functional brain age models together provide a meaningful subject-level summary of brain information for predicting cognition, supporting a multidimensional view of typical aging.

neuroscience↗

bioRxiv2026

Structural integrity moderates the cognitive bearing of dopamine clearance in aging

Age-related cognitive decline in humans is well established, yet how neurobiological variations influence brain dynamics, and subsequently behavioral performance, remains insufficiently explored. Microscale accounts of cognitive aging revolve around attenuations in dopamine (DA) receptors and transporters (DAT). In contrast, macroscale accounts associate individual declines in cognitive performance with functional brain networks' reorganization and white-matter tracts' deterioration. We endeavor to bridge this gap with an amortized dopaminergic aging model (ADAM) which assembles aging brain twins by coupling dopaminergic modulation with structural connectomes to constrain emergent whole-brain dynamics. ADAM thereby augments established DAT decline with personalized estimates, through a reusable and generalizable estimator capable of disentangling neuromodulatory from structural contributions to age-related decline within each individual. To that end, we embed structural connectomes into a low-dimensional space exhibiting high reconstruction fidelity and -pseudonymized- individual identifiability. The spectrum of structural embeddings is subsequently updated with fMRI observations to estimate DA clearance timescales in 3,650 adults aged 36 to 95 years across five independent aging cohorts. Our analysis reveals an age-related elevation of DA clearance timescales. Critically, low performers exhibit elevated clearance in adults with marked structural degradation, and only within sliding age windows centred between 56 and 76 years. This supports a contribution of dopamine to aging, and reveals a structure- and age-conditional association between its clearance and cognitive performance. Our findings suggest that structural connectivity gates the macroscale reach of dopaminergic modulation, thus rendering the gain-control framework of neuromodulation mechanistically explicit across individuals and cohorts. ADAM therefore provides a biologically plausible framework that anchors interindividual variability in cognitive aging to perturbations in the dopaminergic system by learning cohort-scale mappings which can be shared across sites, establishing a transferable testbed for precision neuromodulation and future in-silico pharmacological trials.

neuroscience↗

bioRxiv2026

Linking human brain functional connectivity to underlying neurotransmission

The human brain is organized into interacting functional systems. Their underlying neurobiological mechanisms remain difficult to study in vivo. Here, we adopt a topological framework to quantify the association between neurobiology and brain functional connectivity derived from both resting-state fMRI and MEG. Across six healthy adult cohorts (n = 19-112), regional variation in fMRI connectivity robustly aligns with the distribution of neurotransmitter receptors and transporters. These patterns are present in every single subject, replicate across all cohorts, and are mirrored in MEG. Most prominently, noradrenergic modulation of connectivity in a sensorimotor-insular network is consistently detected across individuals and linked to autonomic arousal. In pharmacological and clinical samples, associations are sensitive to neurotransmitter system manipulation and are altered in early psychosis, aligning with clinical symptomatology. These findings provide biological insight into typical and atypical functional organization of the human brain using a framework linking underlying neurobiology to the functional connectome (NEOFC).

neuroscience↗

bioRxiv2023

Resting state changes in aging and Parkinson's disease are shaped by underlying neurotransmission - a normative modeling study

Human healthy and pathological aging is linked to a steady decline in brain resting state activity and connectivity measures. The neurophysiological mechanisms underlying these changes remain poorly understood. Making use of recent developments in normative modeling and availability of in vivo maps for various neurochemical systems, we test in the UK Biobank cohort (N=25,917) if and how age- and Parkinsons disease related resting state changes in commonly applied local and global activity and connectivity measures co-localize with underlying neurotransmitter systems. We find the distributions of several major neurotransmitter systems including serotonergic, dopaminergic, noradrenergic and glutamatergic neurotransmission to explain age-related changes as observed across functional activity and connectivity measures. Co-localization patterns in Parkinsons disease deviate from normative aging trajectories for these, as well as for cholinergic and GABAergic neurotransmission. The deviation from normal co-localization of brain function and GABAa correlates with disease duration. These findings provide new insights into molecular mechanisms underlying age- and Parkinsons related brain functional changes. Combining normative modeling and neurotransmitter mapping may aid future research and drug development through deeper understanding of neurophysiological mechanisms underlying specific clinical conditions.

neuroscience↗

bioRxiv2022

The Uniqueness of Human Vulnerability to Brain Aging in Great Ape Evolution

Aging is associated with robust decline of the brains gray matter. This spatially specific, morphological change in humans has recently been found in chimpanzees. Direct comparison of age-related brain deterioration between these great ape species can provide a unique evolutionary perspective on human brain aging. Here, we present a data-driven, cross-species comparative framework to explore the relationship between gray matter atrophy with age and cross-species cerebral expansion in chimpanzees and humans. In humans, we found a positive relationship between cerebral aging and cortical expansion, whereas, in chimpanzees no such relationship was found. The greater aging and expansion effects in higher-order cognitive regions like the orbito-frontal cortex were observed to be unique to humans. This resembles the last in, first out hypothesis for neurodevelopment and may represent a biological cost for recent evolutionary developments of human faculties.

neuroscience↗

bioRxiv2022

Towards robust and generalizable super-resolution generative adversarial networks for magnetic resonance neuroimaging: a cross-population approach

Magnetic resonance imaging (MRI) is fundamental to neuroscience, where detailed structural brain scans improve clinical diagnoses and provide accurate neuroanatomical information. Apart from time-consuming scanning protocols, higher image resolution can be obtained with super resolution algorithms. We investigated the generalization abilities of Super Resolution Generative Adversarial Neural Networks (SRGANs) across different populations. T1-weighted scans from three large cohorts were used, spanning older subjects, newborns, and patients with brain tumor- or treatment-induced tissue changes. Upsampling quality was validated using synthetic and anatomical metrics. Models were first trained on each cohort, yielding high image quality and anatomical fidelity. When applied across cohorts, no artifacts were introduced by the SRGANs. SRGANs that were trained on a dataset combining all cohorts also did not induce any population-based artifacts. We showed that SRGANs provide a prime example of robust AI, where application on unseen populations did not introduce artifacts due to training data bias (e.g., insertion or removal of tumor-related signals and contrast inversion). This is an important step in the deployment of SRGANs in real-world settings.

neuroscience↗

bioRxiv2022

The virtual aging brain: a model-driven explanation for cognitive decline in older subjects

Healthy aging is accompanied by heterogeneous decline of cognitive abilities among individuals, especially during senescence. The mechanisms of this variability are not understood, but have been associated with the reorganization of white matter fiber tracts and the functional co-activations of brain regions. Here, we built a causal inference framework to provide mechanistic insight into the link between structural connectivity and brain function, informed by brain imaging data and network modeling. By applying various degrees of interhemispheric degradation of structural connectivity, we were not only able to reproduce the age-related decline in interhemispheric functional communication and the associated dynamical flexibility, but we obtained an increase of global modulation of structural connectivity over the brain function during senescence. Notably, the increase in modulation between structural connectivity and brian function was higher in magnitude and steeper in its increase in older adults with poor cognitive performance. We independently validated the causal hypothesis of our framework via a Bayesian approach based on deep-learning. The current results might be the first mechanistic demonstration of dedifferentiation and scaffolding during aging leading to cognitive decline demonstrated in a large cohort.

neuroscience↗

bioRxiv2020

Genetic variants for head size share genes and pathways with cancer

The size of the human head is determined by growth in the first years of life, while the rest of the body typically grows until early adulthood1. Such complex developmental processes are regulated by various genes and growth pathways2. Rare genetic syndromes have revealed genes that affect head size3, but the genetic drivers of variation in head size within the general population remain largely unknown. To elucidate biological pathways underlying the growth of the human head, we performed the largest genome-wide association study on human head size to date (N = 79,107). We identified 67 genetic loci, 50 of which are novel, and found that these loci are preferentially associated with head size and mostly independent from height. In subsequent neuroimaging analyses, the majority of genetic variants demonstrated widespread effects on the brain, whereas the effects of 17 variants could be localized to one or two specific brain regions. Through hypothesis-free approaches, we find a strong overlap of head size variants with both cancer pathways and cancer genes. Gene set analyses showed enrichment for different types of cancer and the p53, Wnt and ErbB signalling pathway. Genes overlapping or close to lead variants - such as TP53, PTEN and APC - were enriched for genes involved in macrocephaly syndromes (up to 37-fold) and high-fidelity cancer genes (up to 9-fold), whereas this enrichment was not seen for human height variants. This indicates that genes regulating early brain and cranial growth are associated with a propensity to neoplasia later in life, irrespective of height. Our results warrant further investigations of the link between head size and cancer, as well as its clinical implications in the general population.

genomics↗

bioRxiv2020

Occipital Intralobar fasciculi and a novel description of three forgotten tracts

The continuously developing field of magnetic resonance imaging (MRI) has made a considerable contribution to the knowledge of brain architecture. It has given shape to a desire to construct a complete map of functional and structural connections. In particular, diffusion MRI paired with tractography has facilitated a non-invasive exploration of structural brain anatomy, which has helped build evidence for the existence of many association, projection and commissural fiber tracts. However, there is still a scarcity in research studies related to intralobar association fibers. The Dejerines (two of the most notable neurologists of 19th century France) gave an in-depth description of the intralobar fibers of the occipital lobe. Unfortunately, their exquisite work has since been sparsely referred to in the modern literature. This work gives the first modern description of many of the occipital intralobar lobe fibers described by the Dejerines. We perform a virtual dissection and reconstruct the tracts using diffusion MRI tractography. The virtual dissection is guided by the Dejerines treatise, Anatomie des Centres Nerveux. As an accompaniment to this article, the authors provided a French-to-English translation of the treatise portion concerning intra-occipital fiber bundles. This text provides the original description of five intralober occipital tracts, namely: the stratum calcarinum, the stratum proprium cunei, the vertical occipital fasciculus of Wernicke, the transverse fasciculus of the cuneus (TFC) and the transverse fasciculus of the lingual lobe of Vialet. It was possible to reconstruct all these tracts except for the TFC - possibly because its trajectory intermingles with a large amount of other fiber bundles. For completeness, the recently described sledge runner fasciculus, although not one of the Dejerine tracts, was identified and successfully reconstructed.

neuroscience↗

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.

neuroscience↗

bioRxiv2020

The role of thickness inhomogeneities in hierarchical cortical folding

The morphology of the mammalian brain cortex is highly folded. For long it has been known that specific patterns of folding are necessary for an optimally functioning brain. On the extremes, lissencephaly, a lack of folds in humans, and polymicrogyria, an overly folded brain, can lead to severe mental retardation, short life expectancy, epileptic seizures, and tetraplegia. The construction of a quantitative model on how and why these folds appear during the development of the brain is the first step in understanding the cause of these conditions. In recent years, there have been various attempts to understand and model the mechanisms of brain folding. Previous works have shown that mechanical instabilities play a crucial role in the formation of brain folds, and that the geometry of the fetal brain is one of the main factors in dictating the folding characteristics. However, modeling higher-order folding, one of the main characteristics of the highly gyrencephalic brain, has not been fully tackled. The effects of thickness inhomogeneity in the gyrogenesis of the mammalian brain are studied in silico. Finite-element simulations of rectangular slabs are performed. The slabs are divided into two distinct regions, where the outer layer mimics the gray matter, and the inner layer the underlying white matter. Differential growth is introduced by growing the top layer tangentially, while keeping the underlying layer untouched. The brain tissue is modeled as a neo-Hookean hyperelastic material. Simulations are performed with both, homogeneous and inhomogeneous cortical thickness. The homogeneous cortex is shown to fold into a single wavelength, as is common for bilayered materials, while the inhomogeneous cortex folds into more complex conformations. In the early stages of development of the inhomogeneous cortex, structures reminiscent of the deep sulci in the brain are obtained. As the cortex continues to develop, secondary undulations, which are shallower and more variable than the structures obtained in earlier gyrification stage emerge, reproducing well-known characteristics of higher-order folding in the mammalian, and particularly the human, brain.

neuroscience↗

bioRxiv2020

Age-related changes of Peak width Skeletonized Mean Diffusivity (PSMD) across the adult life span: a multi-cohort study

Parameters of water diffusion in white matter derived from diffusion-weighted imaging (DWI), such as fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD and RD), and more recently, peak width of skeletonized mean diffusivity (PSMD), have been proposed as potential markers of normal and pathological brain ageing. However, their relative evolution over the entire adult lifespan in healthy individuals remains partly unknown during early and late adulthood, and particularly for the PSMD index. Here, we gathered and meta-analyzed cross-sectional diffusion tensor imaging (DTI) data from 10 population-based cohort studies in order to establish the time course of white matter water diffusion phenotypes from post-adolescence to late adulthood. DTI data were obtained from a total of 20,005 individuals aged 18.1 to 92.6 years and analyzed with the same pipeline for computing DTI metrics. For each individual MD, AD, RD, and FA mean values were computed over their FA volume skeleton, PSMD being calculated as the 90% peak width of the MD values distribution across the FA skeleton. Mean values of each DTI metric were found to strongly vary across cohorts, most likely due to major differences in DWI acquisition protocols as well as pre-processing and DTI model fitting. However, age effects on each DTI metric were found to be highly consistent across cohorts. RD, MD and AD variations with age exhibited the same U-shape pattern, first slowly decreasing during post-adolescence until the age of 30, 40 and 50, respectively, then progressively increasing until late life. FA showed a reverse profile, initially increasing then continuously decreasing, slowly until the 70s, then sharply declining thereafter. By contrast, PSMD constantly increased, first slowly until the 60s, then more sharply. These results demonstrate that, in the general population, age affects PSMD in a manner different from that of other DTI metrics. The constant increase in PSMD throughout the entire adult life, including during post-adolescence, indicates that PSMD could be an early marker of the ageing process.

neuroscience↗

bioRxiv2019

Genetic factors influencing a neurobiological substrate for psychiatric disorders

A retrospective meta-analysis of magnetic resonance imaging voxel-based morphometry studies proposed that reduced gray matter volumes in the dorsal anterior cingulate and the left and right anterior insular cortex - areas that constitute hub nodes of the salience network - represent a common substrate for major psychiatric disorders. Here, we investigated the hypothesis that the common substrate serves as an intermediate phenotype to detect genetic risk variants relevant for psychiatric disease. To this end, after a data reduction step, we conducted genome-wide association studies of a combined common substrate measure in four population-based cohorts (n=2,271), followed by meta-analysis and replication in a fifth cohort (n=865). After correction for covariates, the heritability of the common substrate was estimated at 0.50 (standard error 0.18). The top single-nucleotide polymorphism (SNP) rs17076061 was associated with the common substrate at genome-wide significance and replicated, explaining 1.2% of the common substrate variance. This SNP mapped to a locus on chromosome 5q35.2 harboring genes involved in neuronal development and regeneration. In follow-up analyses, rs17076061 was not robustly associated with psychiatric disease, and no overlap was found between the broader genetic architecture of the common substrate and genetic risk for major depressive disorder, bipolar disorder, or schizophrenia. In conclusion, our study identified that common genetic variation indeed influences the common substrate, but that these variants do not directly translate to increased disease risk. Future studies should investigate gene-by-environment interactions and employ functional imaging to understand how salience network structure translates to psychiatric disorder risk.

genetics↗

bioRxiv2017

Neural Correlates of Motor Imagery, Action Observation, and Movement Execution: A Comparison Across Quantitative Meta-Analyses

There is longstanding interest in the relationship between motor imagery, action observation, and movement execution. Several models propose that these tasks recruit the same brain regions in a similar manner; however, there is no quantitative synthesis of the literature that compares their respective networks. Here we summarized data from neuroimaging experiments examining Motor Imagery (303 experiments, 4,902 participants), Action Observation (595 experiments, 11,032 participants), and related control tasks involving Movement Execution (142 experiments, 2,302 participants). Motor Imagery recruited a network of premotor-parietal cortical regions, alongside the thalamus, putamen, and cerebellum. Action Observation involved a cortical premotor-parietal and occipital network, with no consistent subcortical contributions. Movement Execution engaged sensorimotor-premotor areas, and the thalamus, putamen, and cerebellum. Comparisons across these networks highlighted key differences in their recruitment of motor cortex,and parietal cortex, and subcortical structures. Conjunction across all three tasks identified a consistent premotor-parietal and somatosensory network. These data amend previous models of the relationships between motor imagery, action observation, and movement execution, and quantify the relationships between their respective networks.\n\nHighlightsO_LIWe compared quantitative meta-analyses of movement imagery, observation, and execution\nC_LIO_LISubcortical structures were most commonly associated with imagery and execution\nC_LIO_LIConjunctions identified a consistent premotor-parietal-somatosensory network\nC_LIO_LIThese data can inform basic and translational work using imagery and observation\nC_LI

neuroscience↗