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Doyon, N.

Publications and source records attributed to Doyon, N..

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A Scoping Review Of Mathematical Models Covering Alzheimer's Disease Progression

Alzheimers disease is a complex, multi-factorial and multi-parametric neurodegenerative etiology. Mathematical models can help understand such a complex problem by providing a way to explore and conceptualize principles, merging biological knowledge with experimental data into a model amenable to simulation and external validation, all without the need for extensive clinical trials. We performed a scoping review of mathematical models of AD with a search strategy applied to the PubMed database which yielded 846 entries. After applying our exclusion criteria, only 17 studies remained from which we extracted data, focusing on three aspects of mathematical modeling: how authors addressed continuous time, how models were solved, and how the high dimensionality and non-linearity of models were managed. Most articles modeled AD at the cellular range of the disease process, operating on a short time scale (e.g., minutes; hours), i.e., the micro view (12/17); the rest considered regional or brain-level processes, with longer timescales (e.g., years, decades) (the macro view). Most papers were concerned primarily with A{beta} (n = 8), few modeled with both A{beta} and tau proteins (n = 3), and some considered more than these two factors in the model (n = 6). Models used partial differential equations (PDEs; n = 3), ordinary differential equations (ODEs; n = 7), both PDEs and ODEs (n = 3). Some didnt specify the mathematical formalism (n = 4). Sensitivity analyses were performed in only a small number of papers (4/17). Overall, we found that only two studies could be considered valid in terms of parameters and conclusions, and two more were partially valid. The majority (n = 13) either was invalid or there was insufficient information to ascertain their status. While mathematical models are powerful and useful tools for the study of AD, closer attention to reporting is necessary to gauge the quality of published studies to replicate or continue with their contributions.

neuroscience↗

Computational modelling of trans-synaptic nanocolumns, a modulator of synaptic transmission

Nanocolumns are trans-synaptic structures which align presynaptic vesicles release sites and postsynaptic receptors. However, how these nano structures shape synaptic signaling remains little understood. Given the difficulty to probe submicroscopic structures experimentally, computer modelling is a usefull approach to investigate the possible functional impacts of nanocolumns. In our in silico model, as has been experimentally observed, a nanocolumn is characterized by a tight distribution of postsynaptic receptors aligned with the presynaptic vesicle release site and by the presence of trans-synaptic molecules which can modulate neurotransmitter diffusion. We found that nanocolumns can play an important role in reinforcing synaptic current mostly when the presynaptic vesicle contains a small number of neurotransmitters. We also show that synapses with and without nanocolumns could have differentiated responses to spontaneous or evoked events. Our work provides a new methodology to investigate in silico the role of the submicroscopic organization of the synapse. Author summaryNeurotransmitter release, diffusion, and binding to postsynaptic receptors are key steps in synaptic transmission. However, the submicroscopic arrangement of receptors and presynaptic sites of neurotransmitter release remains little investigated. Experimental observations revealed the presence of trans-synaptic nanocolumns which span both the pre and post synaptic sites and fine tune the position of the post synaptic receptors. The functional impact of these nanocolumns (i.e. their influence on synaptic current) is both little understood and difficult to investigate experimentally. Here we construct a novel in silico model to investigate the functional impact of nanocolumns and show that they could play a functional role in reinforcing weak synapses.

neuroscience↗