Biology subjects

Luesebrink, F.

Publications and source records attributed to Luesebrink, F..

5 recordsLinked to original sources

bioRxiv2025

Modeling 2D Spatio-Tactile Population Receptive Fields of the Fingertip in Human Primary Somatosensory Cortex

Tactile fingertip sensations are critical for everyday life. Accordingly, tactile fingertip maps have been extensively studied in human primary somatosensory cortex. However, the fine-grained functional architecture of these maps remains largely unknown. To uncover this architecture, we sought to estimate 2D spatio-tactile population receptive fields (pRFs) of the tip of the index finger in human Brodmann area 3b (BA3b). Using functional magnetic resonance imaging at 7T and submillimeter resolution along with prospective motion correction, we recorded brain responses whilst participants sensed a row of vibrotactile pins sweeping along cardinal axes over a portion of the fingertip. To estimate pRF position and size, we initially fit a 2D Gaussian pRF model to the data, which, however, produced largely implausible pRF estimates. Simulations indicated that this likely occurred because the size of pRFs in BA3b surpasses the portion of the fingertip we stimulated, resulting in an incomplete mapping of pRFs. To address this issue, we constrained the fitting procedure and refined the 2D Gaussian pRF model by keeping pRF size constant. Our results for pRF position then revealed that the ulnar-to-radial axis spanning the fingertip maps onto a superior-to-inferior axis in BA3b. Both the putatively large pRF size (relative to the mapping area) and the pRF position gradient we uncover here appear compatible with receptive field properties quantified in monkeys. Our study provides the first comprehensive investigation into the fine-grained functional architecture of human fingertip maps and brings us one step closer to a thorough understanding thereof.

neuroscience↗

bioRxiv2024

SMAS: Structural MRI-Based AD Score using Bayesian VAE

This study introduces the Structural MRI-based Alzheimers Disease Score (SMAS), a novel index intended to quantify Alzheimers Disease (AD)-related morphometric patterns using a deep learning Bayesian-supervised Variational Autoencoder (Bayesian-SVAE). SMAS index was constructed using baseline structural MRI data from the DELCODE study and evaluated longitudinally in two independent cohorts: DEL-CODE (n=415) and ADNI (n=190). Our findings indicate that SMAS has strong associations with cognitive performance (DELCODE: r=-0.83; ADNI: r=-0.62), age (DEL-CODE: r=0.50; ADNI: r=0.28), hippocampal volume (DEL-CODE: r=-0.44; ADNI: r=-0.66), and total grey matter volume (DELCODE: r=-0.42; ADNI: r=-0.47), suggesting its potential as a biomarker for AD-related brain atrophy. Moreover, our longitudinal studies suggest that SMAS may be useful for early identification and tracking of AD. The model demonstrated significant predictive accuracy in distinguishing cognitively healthy individuals from those with AD (DELCODE: AUC=0.971 at baseline, 0.833 at 36 months; ADNI: AUC=0.817 at baseline, improving to 0.903 at 24 months). Notably, over a 36-month period, SMAS index outperformed existing measures such as SPARE-AD and hippocampal volume. Relevance map analysis revealed significant morphological changes in key AD-related brain regions--including the hippocampus, posterior cingulate cortex, precuneus, and lateral parietal cortex--highlighting that SMAS is a sensitive and interpretable biomarker of brain atrophy, suitable for early AD detection and longitudinal monitoring of disease progression.

neuroscience↗

bioRxiv2024

Dysfunction of the episodic memory network in the Alzheimer's disease cascade

Alzheimers disease (AD) is a major cause of dementia and cognitive decline. Here we assessed how episodic memory circuit dysfunction, a hallmark of AD, is related to the longitudinal cascade of AD biomarkers, neurodegeneration and cognition using data from the DZNE Longitudinal Cognitive Impairment and Dementia study. This data set is unique by including over 1000 longitudinal functional magnetic resonance imaging (fMRI) measurements during episodic memory encoding. We leveraged a disease progression model (DPM) to obtain AD progression scores. Voxel-wise analyses revealed widespread loss of deactivation (hyperactivation) and activation (hypoactivation) with increasing disease stage. Hyperactivation trajectories were nonlinear and visually preceded trajectories of cognition. Overall, hyperactivation was independently associated with co-occurrence of amyloid- and tau-positivity and neurodegeneration, suggesting synaptic dysfunction and neurodegeneration as two independent drives of cognitive decline. Our results therefore provide evidence for a critical time window in which pharmacological treatments targeting the synapse may improve cognition.

neuroscience↗

bioRxiv2023

Diagnostic images for mild cognitive impairment reveal biomarker status and abnormal scene processing

Research on the impairment of episodic memory in Alzheimers disease often focuses on the processes of memory rather than the content of the specific images being remembered. We recently showed that patients with mild cognitive impairment (MCI), Stage 3 of Alzheimers disease, can memorize certain images quite well, suggesting that episodic memory is not uniformly impaired. Certain images, on the other hand, could not be memorized by MCI patients and were instead diagnostic for distinguishing MCI from healthy older adults. In this study, we investigate whether poor memory for diagnostic images is related to impaired neural processing in specific brain regions due to Alzheimers biomarker pathology. 64 healthy controls and 48 MCI participants in the DELCODE dataset performed a visual scene memory task during fMRI, with CSF Alzheimers disease biomarker data collected (i.e., amyloid and tau biomarkers). We found that diagnostic images have larger behavior-biomarker correlations for total tau, phospho-tau, A{beta}42/A{beta}40, A{beta}42/phospho-tau compared to non-diagnostic images, suggesting that memory for these specific images are more affected by Alzheimers disease pathology. The fMRI data revealed an interaction effect between group membership (healthy control / MCI) and image diagnosticity (diagnostic / non-diagnostic scene images), with MCI participants having higher activation in scene processing regions (parahippocampal place area, retrosplenial cortex and occipital place area) for diagnostic images than non-diagnostic images. In contrast, healthy controls showed no differences in processing between diagnostic and non-diagnostic images. These results suggest that MCI individuals may engage in inefficiently heightened encoding activation for these diagnostic images. Our results show that special "diagnostic" images exist that can reveal amyloid and tau pathology and differences in neural activity in scene regions.

neuroscience↗

bioRxiv2021

Perceived and mentally rotated contents are differentially represented in cortical depth of V1

Primary visual cortex (V1) in humans is known to represent both veridically perceived external input and internally-generated contents underlying imagery and mental rotation. However, it is unknown how the brain keeps these contents separate thus avoiding a mixture of the perceived and the imagined which could lead to potentially detrimental consequences. Inspired by neuroanatomical studies showing that feedforward and feedback connections in V1 terminate in different cortical layers, we hypothesized that this anatomical compartmentalization underlies functional segregation of external and internally-generated visual contents, respectively. We used high-resolution layer-specific fMRI to test this hypothesis in a mental rotation task. We found that rotated contents were predominant at outer cortical depth bins (i.e. superficial and deep). At the same time perceived contents were represented stronger at the middle cortical bin. These results identify how through cortical depth compartmentalization V1 functionally segregates rather than confuses external from internally-generated visual contents. These results indicate that feedforward and feedback manifest in distinct subdivisions of the early visual cortex, thereby reflecting a general strategy for implementing multiple cognitive functions within a single brain region.

neuroscience↗