Biology subjects

Heneka, M.

Publications and source records attributed to Heneka, M..

3 recordsLinked to original sources

bioRxiv2026

Oligodendrocyte subtype diversity underlines clinical progression in Parkinsons disease

Despite growing evidence for glial involvement in Parkinsons disease, oligodendrocyte dysfunction remains poorly defined. To address this gap, we compared single-cell RNA sequencing from a mouse model of -synuclein aggregation pathology with fresh human brain tissue from deep brain stimulation surgery to build a cross-species framework of disease progression. In total, we profiled over 200,000 cortical transcriptomes, including 55,000 oligodendrocytes. Early disease in mice was characterized by inflammatory activation, while advanced stages in both species converged on metabolic dysfunction, including impaired ribosomal output, chaperone stress responses, ubiquitination deficits, and lysosomal perturbation. In patients, APLP1 was upregulated and correlated with clinical disease progression and increased levodopa demand, linking -synuclein spread in oligodendrocytes to disease severity. APP and CNTN pathways emerged as key signalling axes, with CNTN reflecting weakened reparative communication and reduced resilience. Together, these findings define oligodendrocyte subtype dynamics as shared and clinically relevant features of PD progression.

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↗

bioRxiv2019

Memorability of photographs in subjective cognitive decline and mild cognitive impairment: implications for cognitive assessment

INTRODUCTIONImpaired long-term memory is a defining feature of Mild Cognitive Impairment (MCI). We tested whether this impairment is item-specific, limited to some memoranda whereas some remain consistently memorable.\n\nMETHODSWe conducted item-based analyses of long-term visual recognition memory. 394 participants (healthy controls (HC), Subjective Cognitive Decline (SCD), and MCI) in the multicentric DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) were tested with images from a pool of 835 photographs.\n\nRESULTSWe observed consistent memorability for images in HCs, SCDs, and MCI, predictable by a neural network trained on another healthy sample. Looking at memorability differences between groups, we identified images that could successfully categorize group membership with higher success and a substantial image reduction than the original image set.\n\nDISCUSSIONIndividuals with SCD and MCI show consistent memorability for specific items, while other items show significant diagnosticity. Certain stimulus features could optimize diagnostic assessment, while others could support memory.

neuroscience↗