Biology subjects

Rigoux, L.

Publications and source records attributed to Rigoux, L..

3 recordsLinked to original sources

bioRxiv2024

Putaminal dopamine modulates movement motivation in Parkinson's disease

The relative inability to produce effortful movements (akinesia) is the most specific motor sign of Parkinsons disease. The motor motivation hypothesis suggests that akinesia may not reflect a deficiency in motor control per se, but a deficiency in cost-benefit considerations for motor effort. For the first time, we investigated the quantitative effect of dopamine depletion on the motivation of motor effort in Parkinsons disease. A total of 21 patients with Parkinsons disease and 26 healthy controls were included. An incentivized force task was used to capture the amount of effort participants were willing to invest for different monetary incentive levels and dopamine transporter depletion in the bilateral putamen was assessed. Our results demonstrate that patients with Parkinsons disease applied significantly less grip force than healthy controls, especially for low incentive levels. Congruously, decrease of motor effort with greater loss of putaminal dopaminergic terminals was most pronounced for low incentive levels. This signifies that putaminal dopamine is most critical to motor effort when the trade-off with the benefit is poor. Taken together, we provide direct evidence that the reduction of effortful movements in Parkinsons disease depends on motivation and that this effect is associated with putaminal dopaminergic degeneration.

neuroscience↗

bioRxiv2024

Bayesian Workflow for Generative Modeling in Computational Psychiatry

Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been suggested which, however, has rarely been applied in Translational Neuromodeling and Computational Psychiatry (TN/CP) so far. Here, we present a worked example of Bayesian workflow in the context of a typical application scenario for TN/CP. This application example uses Hierarchical Gaussian Filter (HGF) models, a family of computational models for hierarchical Bayesian belief updating. When equipped with a suitable response model, HGF models can be fit to behavioural data from cognitive tasks; these data frequently consist of binary responses and are typically univariate. This poses challenges for statistical inference due to the limited information contained in such data. We present a novel set of response models that allow for simultaneous inference from multivariate (here: two) behavioural data types. Using both simulations and empirical data from a speed-incentivised associative reward learning (SPIRL) task, we show that harnessing information from two different data streams (binary responses and continuous response times) improves the accuracy of inference (specifically, identifiability of parameters and models). Moreover, we find a linear relationship between log-transformed response times in the SPIRL task and participants uncertainty about the outcome. Our analysis illustrates the benefits of Bayesian workflow for a typical use case in TN/CP. We argue that adopting Bayesian workflow for generative modelling helps increase the transparency and robustness of results, which in turn is of fundamental importance for the long-term success of TN/CP.

neuroscience↗

bioRxiv2023

Preserved striatal innervation and motor function despite severe loss of nigral dopamine neurons following mitochondrial dysfunction induced by mtDNA mutations

Degeneration of dopamine neurons in the substantia nigra and their striatal axon terminals causes cardinal motor symptoms of Parkinsons disease (PD). In idiopathic cases, high levels of mitochondrial DNA (mtDNA) mutations associated with mitochondrial dysfunction are a central feature of these vulnerable neurons. Here we present a mouse model expressing the K320E-variant of the mitochondrial helicase Twinkle in dopamine neurons, leading to accelerated mtDNA ageing. K320E-TwinkleDaN mice showed normal motor function at 20 months of age, although already [~]70% of nigral dopamine neurons had perished. The remaining neuron population still preserved [~]75% of axon terminals in the dorsal striatum, which enabled normal dopamine release. Transcriptome analysis and viral tracing confirmed compensatory axonal sprouting of surviving nigral dopamine neurons. We conclude that a small population of substantia nigra neurons can adapt to mtDNA mutations and maintain motor control in mice, holding chances for new treatment strategies in PD patients.

neuroscience↗