Biology subjects

Bittner, N.

Publications and source records attributed to Bittner, N..

4 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↗

bioRxiv2024

Butterfly eggs prime anti-herbivore defense in an annual but not perennial Arabidopsis species

While plant anti-herbivore defenses of the annual plant species Arabidopsis thaliana were shown to be primable by Pieris brassicae eggs, the primability of the phylogenetically closely related perennial Arabidopsis lyrata has not yet been investigated. Previous studies revealed that closely related wild Brassicaceae plant species, the annual Brassica nigra and the perennial Brassica oleracea, exhibit an egg-primable defense trait, even though they have different life spans. Here, we tested whether P. brassicae eggs prime anti-herbivore defenses of the perennial A. lyrata. We exposed A. lyrata to P. brassicae eggs and larval feeding and assessed their primability by i) determining the biomass of P. brassicae larvae after feeding on plants with and without prior P. brassicae egg deposition and ii) investigating the plant transcriptomic response after egg deposition and/or larval feeding. For comparison, these studies were also conducted with A. thaliana. Consistent with previous findings, A. thalianas response to prior P. brassicae egg deposition negatively affected conspecific larvae feeding upon A. thaliana. However, this was not observed in A. lyrata. Arabidopsis thaliana responded to P. brassicae eggs with strong transcriptional reprogramming, whereas A. lyrata responses to eggs were negligible. In response to larval feeding, A. lyrata exhibited a greater transcriptome change compared to A. thaliana. Among the strongly feeding-induced A. lyrata genes were those that are egg-primed in feeding-induced A. thaliana, i.e., CAX3, PR1, PR5 and PDF1.4. These results suggest that A. lyrata compensates for its lack of egg-mediated primability by a stronger response to larval feeding.

plant biology↗

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↗