Biology subjects

Lenders, A.

Publications and source records attributed to Lenders, A..

3 recordsLinked to original sources

bioRxiv2026

Learning sculpts microstructure in real time: evidence from dense temporal sampling of diffusion MRI

Learning induces rapid microstructural plasticity in the human brain, yet the precise temporal dynamics of these changes remain unclear. Using dense temporal sampling of diffusion-weighted MRI (DW-MRI) combined with task-based fMRI, we assessed microstructural changes throughout a declarative learning paradigm and subsequent rest. Seventy-four participants (36 females) learned image-location associations across four encoding-retrieval repetitions while undergoing interleaved functional and DW-MRI acquisitions. A matched control group (N=37, 21 females) underwent a similar imaging protocol without learning. Dense sampling of DW-MRI acquisitions (k=2146 across 22 time points in 127 min) revealed that learning-induced mean diffusivity (MD) decreases emerged shortly after learning onset and continued to develop during post-learning rest. The most robust and spatially consistent change was localized to the left middle occipital/temporal gyrus, a region also showing functional activation during encoding and retrieval. Linear mixed-effects modeling further confirmed a significant group-by-time interaction, with MD reductions in the left middle occipital/temporal gyrus emerging as early as {approx}7 min after learning onset, becoming robust by {approx}35-40 min, and persisting throughout the extended post-learning period, while controls showed no changes. Our findings demonstrate that learning-related microstructural plasticity unfolds continuously from encoding to offline consolidation, with learning-induced structural changes emerging in functionally engaged regions. Dense temporal sampling of DW-MRI offers a powerful approach to bridge functional activation and structural remodeling, providing evidence of when and where experience-dependent plasticity occurs during memory formation.

neuroscience↗

bioRxiv2026

Rapid expansion and renormalization of parietal gray matter volume following associative learning

The human brain exhibits rapid structural plasticity following learning, yet its temporal dynamics and behavioral relevance remain elusive, particularly for declarative forms of learning. In this study, we tested whether T1-weighted MRI captures rapid structural reorganization following associative memory formation and whether such changes follow an expansion-renormalization trajectory. We combined three independent datasets (N = 198) to quantify gray matter volume (GMV) changes following an associative memory task using voxel-based morphometry across baseline, 2 h, and 12 h post-learning. We observed transient GMV increases in parietal, lateral occipital, and cerebellar regions at 2 h post-learning, which were no longer detectable relative to baseline by 12 h, consistent with rapid renormalization. Critically, GMV changes in left parietal cortex were associated with memory retention, such that greater maintenance of GMV was associated with better retention. In a separate, spatially distinct effect, sleep facilitated renormalization of GMV in the right parietal cortex, an effect that showed no association with memory retention. Our findings provide evidence that human gray matter undergoes hour-scale changes, reflecting dissociable memory- and sleep-related components.

neuroscience↗

bioRxiv2025

Investigating the temporal dynamics and modelling of mid-level feature representations in humans

Scene perception is a key function of biological visual systems. According to the hierarchical processing view, scene perception in the human brain begins with low-level features, progresses to mid-level features, and ends with high-level features. While low- and high-level feature processing is well-studied, research on mid-level features remains limited. Here, we addressed this gap by investigating when mid-level features are processed in humans using a novel stimulus set of naturalistic scenes as images and videos, accompanied with ground-truth annotations for five mid-level features (reflectance, lighting, world normals, scene depth and skeleton position), and two framing features: one low-level (edges) and one high-level feature (action). To reveal when low-, mid- and high-level features are represented in the brain, we collected electroencephalography (EEG) data from human participants during stimulus presentation and trained encoding models to predict EEG data from ground-truth annotations. We revealed that mid-level features were best represented between [~]100 and [~]250 ms post-stimulus, between low- and high-level features. Moreover, we assessed scene- and action-trained convolutional neural networks (CNNs) as models of mid-level feature processing in humans. We found a comparable processing order for mid-but not low- or high-level features with humans. Overall, our results characterize mid-level feature processing in humans in the temporal domain and reveal CNNs as suitable models of the processing hierarchy of mid-level vision in humans.

neuroscience↗