Biology subjects

Rupprecht, R.

Publications and source records attributed to Rupprecht, R..

6 recordsLinked to original sources

bioRxiv2023

Chronic administration of XBD173 ameliorates cognitive deficits and neuropathology via 18 kDa translocator protein (TSPO) in a mouse model of Alzheimer disease

Alzheimers disease (AD) is characterized by the accumulation of {beta}-amyloid peptide (A{beta}). There is increasing evidence that depression may precede AD and may be an early manifestation of dementia, suggesting common mechanisms underlying both diseases. Ligands targeting the mitochondrial translocator protein (18 kDa) (TSPO), promote neurosteroidogenesis and may be neuroprotective. Moreover, TSPO is upregulated in AD. To study whether the TSPO ligand XBD173 may exert early neuroprotective effects in AD pathology we investigated the impact of XBD173 on amyloid toxicity and neuroplasticity in mouse models. We show that XBD173 (emapunil), via neurosteroid-mediated signaling via delta subunit-containing GABAA receptors, prevents the neurotoxic effect of A{beta} on long-term potentiation (CA1-LTP) in the hippocampus and prevents the loss of spines. Chronic but not acute administration of XBD173 ameliorates spatial learning deficits in transgenic AD mice with arctic mutation (ArcA{beta}) mice. The heterozygous TSPO-knockout crossed with the transgenic arctic mutation model of AD mice (het TSPOKO X ArcA{beta}) treated with XBD173 does not show this improvement in spatial learning suggesting TSPO is needed for procognitive effects of XBD173. The neuroprotective profile of XBD173 in AD pathology is further supported by a reduction in plaques and soluble A{beta} levels in the cortex, increased synthesis of neurosteroids, rescued spine density, reduction of complement protein C1q deposits, and reduced astrocytic phagocytosis of functional synapses both in the hippocampus and cortex. Our findings suggest that XBD173 may exert therapeutic effects via TSPO in a mouse model of AD.

neuroscience↗

bioRxiv2022

Long-Short Term Memory Networks for Electric Source Imaging with Distributed Dipole Models

1Magneto- and electroencephalography (M/EEG) are widespread techniques to measure neural activity in-vivo at a high temporal resolution but relatively low spatial resolution. Locating the sources underlying the M/EEG poses an inverse problem, which is itself ill-posed. In recent years, a new class of source imaging methods was developed based on artificial neural networks. We present a long-short term memory (LSTM) network to solve the M/EEG inverse problem. It integrates low computational cost, exploitation of both the coarse spatial but also the excellent temporal information from the EEG, input flexibility and robustness to noise. We compared the LSTM network with classical inverse solutions using both simulation data and real EEG data, recorded in epileptic patients during intracranial stimulation. The LSTM network shows higher accuracy on multiple metrics and for varying numbers of neural sources, compared to classical inverse solutions but also compared to our alternative architecture without integration of temporal information. The performance of the LSTM network regarding its robustness to noise and low localization errors renders it a promising inverse solution to be considered in future source localization studies and for clinical applications.

neuroscience↗

bioRxiv2021

Induced neural progenitor cells and iPS-neurons from major depressive disorder patients show altered bioenergetics and electrophysiological properties

The molecular pathomechanisms of major depressive disorder (MDD) are still not completely understood. Here, we follow the hypothesis, that mitochondria dysfunction which is inevitably associated with bioenergetic disbalance is a risk factor that contributes to the susceptibility of an individual to develop MDD. Thus, we investigated molecular mechanisms related to mitochondrial function in induced neuronal progenitor cells (NPCs) which were reprogrammed from fibroblasts of eight MDD patients and eight non-depressed controls. We found significantly lower maximal respiration rates, altered cytosolic basal calcium levels, and smaller soma size in NPCs derived from MDD patients. These findings are partially consistent with our earlier observations in MDD patient-derived fibroblasts. Furthermore, we differentiated MDD and control NPCs into iPS-neurons and analysed their passive biophysical and active electrophysiological properties to investigate whether neuronal function can be related to altered mitochondrial activity and bioenergetics. Interestingly, MDD patient-derived iPS-neurons showed significantly lower membrane capacitance, a less hyperpolarized membrane potential, increased Na+ current density and increased spontaneous electrical activity. Our findings indicate that functional differences evident in fibroblasts derived from MDD patients are partially present after reprogramming to induced-NPCs, might relate to altered function of iPS-neurons and thus might be associated with the aetiology of major depressive disorder.

neuroscience↗

bioRxiv2021

Effects of Escitalopram and Relearning on Cortical and Subcortical Grey Matter in Healthy Humans

The antidepressant effect of selective serotonin reuptake inhibitors (SSRI) is related to increased neuroplasticity during relearning. Stress-induced dendritic atrophy in key brain areas for learning and memory such as the hippocampus and prefrontal cortex is reversed by SSRI treatment. This finding is accompanied by behavioral stabilization. The aim of this study was to investigated serotonergic modulation effects on structural neuroplasticity (cortical thickness, subcortical volumes) during relearning in healthy subjects. Participants performed daily associative learning tasks over 3 weeks followed by a 3-week relearning phase combined with intake of the SSRI escitalopram or placebo. Evidence suggests that SSRIs promote the brains susceptibility to change on the basis of environment factors. We found no effect of SSRI on grey matter measures during relearning. Here, non-findings might be a consequence of the implemented intensity and duration of study interventions. With sparse literature on healthy participants in this field, future studies will have to further elucidate SSRIs properties on relearning and structural neuroplasticity.

neuroscience↗

bioRxiv2020

Supracategorical fear information revealed by aversively conditioning multiple categories

Fear-generalization is a critical function for survival, in which an organism extracts information from a specific instantiation of a threat (e.g., the western diamondback rattlesnake in my front yard on Sunday) and learns to fear--and accordingly respond to--pertinent higher-order information (e.g., snakes live in my yard). Previous work investigating fear-conditioning in humans has used functional magnetic resonance imaging (fMRI) to demonstrate that activity-patterns of stimuli from an aversively-conditioned category (CS+) are more similar to each other than those of a neutral category (CS-). Here we designed a three-phase (i.e., baseline, conditioned, extinction) experiment using fMRI and multiple aversively-conditioned categories to ask whether we would find only similarity increases within the CS+ categories or also an increase in similarity between the CS+ categories. Using representational similarity analysis, we correlated a set of models to activity-patterns underlying several regions of interest and found that, following fear-conditioning, between-category and within-category similarity increased for the CS+ categories in the superior frontal gyrus (SFG) and the right temporal pole (rTP). Activity patterns in the object-selective lateral occipital cortex tended to prefer the semantic model, regardless of the experimental phase. These results advance prior pattern-based neuroimaging work by exploring the effect of aversively-conditioning multiple categories and indicate an extended role for the SFG and rTP in potentially linking discrete information or abstractly representing supracategorical information during fear-learning for the purpose of proper generalization.

neuroscience↗

bioRxiv2020

ConvDip: A convolutional neural network for better M/EEG Source Imaging

1The EEG is a well-established non-invasive method in neuroscientific research and clinical diagnostics. It provides a high temporal but low spatial resolution of brain activity. In order to gain insight about the spatial dynamics of the EEG one has to solve the inverse problem, i.e. finding the neural sources that give rise to the recorded EEG activity. The inverse problem is ill-posed, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipoles sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods on all focused performance measures. It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. It produces plausible inverse solutions for real EEG recordings from human participants. (4) The trained network needs less than 40 ms for a single prediction. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG data, with high relevance for clinical applications, e.g. in epileptology and real time applications.

neuroscience↗