Biology subjects

Kimmich, O.

Publications and source records attributed to Kimmich, O..

3 recordsLinked to original sources

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

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↗

bioRxiv2022

Individualized Gaussian Process-based Prediction of Memory Performance and Biomarker Status in Ageing and Alzheimer's disease

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as informative covariates, vascular risks, brain activity, neuropsychological test etc.,) might provide useful predictions of clinical outcomes during progression towards Alzheimers disease (AD). The Bayesian approach aims to provide a trade-off by employing relevant features combinations to build decision support systems in clinical settings where uncertainties are relevant. We tested the approach in the MRI data across 959 subjects, aged 59-89 years and 453 subjects with available neuropsychological test scores and CSF biomarker status (amyloid-beta (A{beta})42/40 & and phosphorylated tau (pTau)) from a large sample multi-centric observational cohort (DELCODE). In order to explore the beneficial combinations of information from different sources, we presented a MRI-based predictive modelling of memory performance and CSF biomarker status (positive or negative) in the healthy ageing group as well as subjects at risk of Alzheimers disease using a Gaussian process multikernel framework. Furthermore, we systematically evaluated predictive combinations of input feature sets and their model variations, i.e. (A) combinations of brain tissue classes and feature type (modulated vs. unmodulated), choices of filter size of smoothing (ranging from 0 to 15 mm full width at half maximum), and image resolution (1mm, 2mm, 4mm and 8mm); (B) incorporating demography and covariates (C) the impact of the size of the training data set (i.e., number of subjects); (D) the influence of reducing the dimensions of data and (E) choice of kernel types. Finally, the approach was tested to reveal individual cognitive scores at follow-up (up to 4 years) using the baseline features. The highest accuracy for memory performance prediction was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF-biomarkers explaining 57% of outcome variance in out of sample predictions. The best accuracy for A{beta}42/40 status classification was achieved for combination demographics, ApoE4 and memory score while usage of structural MRI improved the classification of individual patients pTau status.

neuroscience↗