Biology subjects

Lüsebrink, F.

Publications and source records attributed to Lüsebrink, F..

3 recordsLinked to original sources

bioRxiv2025

Disrupted Forward Connectivity in Parieto-temporal Network Impairs Memory Performance in Alzheimer's Disease

Alzheimers disease (AD) is characterised by the accumulation of beta-amyloid (A{beta}) and tau proteins, leading to neurodegeneration and cognitive decline. While A{beta} and tau are known to disrupt synaptic function, the mechanisms linking these molecular pathologies to network-level dysfunction and memory impairment remain poorly understood. Here we investigated the effects of A{beta} and tau pathology (CSF A{beta}42/40 ratio and tau phosphorylated at position 181, p-tau-181, respectively) on effective connectivity (EC) related to memory encoding, which may constitute a link between synaptic pathology and cognitive outcomes. Functional magnetic resonance imaging (fMRI) during visual memory encoding was acquired from participants of the multicentric DZNE Longitudinal Cognitive Impairment and Dementia Study (DELCODE), including 203 cognitively normal older participants (CN) as well as individuals with subjective cognitive decline (SCD; N = 204), mild cognitive impairment (MCI; N = 65), and early dementia due to AD (DAT; N = 21). EC was assessed by applying Dynamic causal modelling (DCM) to the fMRI data, using brain regions previously implicated in memory-encoding: the parahippocampal place area (PPA), the hippocampus (HC) and the precuneus (PCU). Disruptions in forward connectivity from the PPA to the HC and PCU were associated with both memory impairment and indices of AD pathology. Specifically, reduced excitatory EC from the PPA to the HC was associated with higher p-tau-181 levels and correlated with poorer memory performance. Diminished inhibitory EC from the PPA to the PCU was driven by both tau and amyloid pathology and was likewise linked to memory decline. Our findings suggest that disrupted forward connectivity within the temporo-parietal memory network constitutes a candidate mechanism mediating the relationship between molecular pathology and cognitive dysfunction.

neuroscience↗

bioRxiv2024

Fully Automated MRI-based Analysis of the Locus Coeruleus in Aging and Alzheimer's Disease Dementia using ELSI-Net

INTRODUCTIONThe Locus Coeruleus (LC) is linked to the development and pathophysiology of neurodegenerative diseases such as Alzheimers Disease (AD). Magnetic Resonance Imaging based LC features have shown potential to assess LC integrity in vivo. METHODSWe present a Deep Learning based LC segmentation and feature extraction method: ELSI-Net and apply it to healthy aging and AD dementia datasets. Agreement to expert raters and previously published LC atlases were assessed. We aimed to reproduce previously reported differences in LC integrity in aging and AD dementia and correlate extracted features to cerebrospinal fluid (CSF) biomarkers of AD pathology. RESULTSELSI-Net demonstrated high agreement to expert raters and published atlases. Previously reported group differences in LC integrity were detected and correlations to CSF biomarkers were found. DISCUSSIONAlthough we found excellent performance, further evaluations on more diverse datasets from clinical cohorts are required for a conclusive assessment of ELSI-Nets general applicability. HighlightsO_LIthorough evaluation of a fully automatic LC segmentation method termed ELSI-Net in aging and AD dementia C_LIO_LIELSI-Net outperforms previous work and shows high agreement with manual ratings and previously published LC atlases C_LIO_LIELSI-Net replicates previously shown LC group differences in aging and AD C_LIO_LIELSI-Nets LC volume correlates with CSF biomarkers of AD pathology C_LI RESEARCH IN CONTEXTO_LISystematic Review: The authors reviewed the literature using traditional sources (e.g. Pubmed, Google Scholar). Although there are several publications introducing semi-automatic methods for LC segmentation, the application of Deep Learning methods is underexplored. To the best of our knowledge, this is the first paper using a Deep Learning based approach for automated LC segmentation in AD dementia. C_LIO_LIInterpretation: Our work introduces and evaluates an improved automatic, Deep Learning based LC segmentation and analysis approach. The results suggest a very high potential for practical applicability, e.g. in large-scale clinical studies for neurodegenerative diseases. C_LIO_LIFuture Directions: ELSI-Net can be used to assess LC integrity on large- or small-scale studies in Alzheimers Disease dementia. To ensure robust performance, ELSI-Net should be further evaluated in larger, more diverse datasets comprising varying LC MRI protocols and clinical populations. C_LI

bioinformatics↗

bioRxiv2021

It is the Locus Coeruleus! Or... is it? : A proposition for analyses and reporting standards for structural and functional magnetic resonance imaging of the noradrenergic Locus Coeruleus

The noradrenergic locus coeruleus (LC) in the brainstem shows early signs of protein pathologies in neurodegenerative diseases such as Alzheimers and Parkinsons disease. As the LCs small size (approximately 2.5 mm in width) presents a challenge for molecular imaging, the past decade has seen a steep rise in structural and functional Magnetic Resonance (MR) studies aiming to characterise the LCs changes in ageing and neurodegeneration. However, given its position in the brainstem and small volume, great care must be taken to yield methodologically reliable MR results as spatial deviations in transformations can greatly reduce the statistical power of the analyses at the group level. Here, we suggest a spatial transformation procedure and a set of quality assessment methods which allow LC researchers to achieve the spatial precision necessary for investigating this small but potentially impactful brain structure. Using a combination of available toolboxes (SPM12, ANTs, FSL, FreeSurfer), individual structural and functional 3T LC scans are transformed into MNI space via a study-specific anatomical template. Following this, the precision of spatial alignment in individual MNI-transformed images is quantified using in-plane distance measures based on slice-specific centroids of structural LC segmentations and based on landmarks of salient anatomical features in mean functional images, respectively. Median in-plane distance of all landmarks on the transformed structural as well as functional LC imaging data were below 2 mm, thereby falling below the typical LC width of 2.5 mm suggested by post-mortem data. With the set of spatial post-processing steps outlined in this paper and available for download, we hope to give readers interested in LC imaging a starting point for a reliable analysis of structural and functional MR data of the LC and to have also taken a first step towards establishing reporting standards of LC imaging data.

neuroscience↗