Krohn, F. | bioRxiv Science

bioRxiv2025

Hippocampal-cortical connectivity relates to inter-individual differences and training gains in distinguishing similar memories

Mnemonic discrimination (MD) is the ability to distinguish current experiences from similar memories. Research on the brain correlates of MD has focused on how regional neural responses are linked to MD. Here we go beyond this approach to investigate inter-regional functional connectivity patterns related to MD, its inter-individual variability and training-related improvement. Based on prior work we focused on medial temporal lobe (MTL), prefrontal cortex (PFC) and visual regions. We used fMRI to determine how functional connectivity patterns between these regions are related to MD before and after 2-weeks of web-based cognitive training. We identified a functional connectivity signature involving MTL-PFC-visual areas during successful MD. We found that hippocampal-PFC connectivity was negatively associated with interindividual variability in MD performance across two different tasks. Hippocampal-PFC connectivity decrease was also linked to interindividual variability in post-training MD improvement. Additionally, training led to increased connectivity from the lateral occipital cortex to the occipital pole area. Our results point to a hippocampal-PFC connectivity pattern which is a reliable, task-invariant, marker of MD performance. This pattern is further related to MD training gains providing causal evidence for its relevance in distinguishing similar memories. Overall, we show that hippocampal-PFC connectivity constitutes a neural resource for MD that enables training-related improvements and could be targeted in future research to enhance cognition.

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↗