Biology subjects

Lohse, A.

Publications and source records attributed to Lohse, A..

4 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

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↗

bioRxiv2022

Linking impulsivity to activity levels in pre-supplementary motor area during sequential gambling

Impulsivity refers to the tendency to act prematurely or without forethought, and excessive impulsivity is a key problem in many neuropsychiatric disorders. Since the pre-supplementary motor area (preSMA) has been implicated in inhibitory control, this region may also contribute to impulsivity. Here, we examined whether functional recruitment of preSMA may contribute to risky choice behavior (state impulsivity) during sequential gambling and its relation to self-reported trait impulsivity. To this end, we performed task-based functional MRI (fMRI) after low-frequency (1 Hz) repetitive transcranial magnetic stimulation (rTMS) of the preSMA. We expected low-frequency rTMS to modulate task-related engagement of the preSMA and hereby, tune the tendency to make risky choices. 24 healthy volunteers (12 females, 19-52 years) received real or sham rTMS on separate days in counterbalanced order. Thereafter, participants performed a sequential gambling task with concurrently increasing stakes and risk during whole-brain fMRI. In the sham-rTMS session, self-reported trait impulsivity scaled positively with state impulsivity (riskier choice behavior) during gambling. The higher the trait-impulsivity, the lower was the task-related increase in preSMA activity with increasingly risky choices. Following real-rTMS, low-impulsivity participants increased their preference for risky choices, while the opposite was true for high-impulsivity participants resulting in an overall decoupling of trait impulsivity and state impulsivity during gambling. This rTMS-induced behavioral shift was mirrored in the rTMS-induced change in preSMA activation. These results provide converging evidence for a causal link between the level of task-related preSMA activity and the propensity for impulsive risk-taking behavior in the context of sequential gambling. Significance statementImpulsivity is a personal trait characterized by a tendency to act prematurely or without forethought, and excessive impulsivity is a key problem in many neuropsychiatric disorders. The pre-supplementary motor area (preSMA) has been implicated in inhibitory control. Here, we provide evidence that this region contributes to the implementation of general impulsive tendencies (trait impulsivity) into actual behavior (state impulsivity). When healthy volunteers performed a sequential gambling task, their choice behavior (i.e., state impulsivity) correlated positively with their impulsivity score (i.e, trait impulsivity). Additionally, participants with lower trait impulsivity showed a stronger increase in task-related activity of the preSMA with increasing risk. Both of these relationships were uncoupled after perturbing the preSMA with repetitive transcranial stimulation (rTMS).

neuroscience↗

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↗