Development of Novel Models to Study Deep Brain Effects of Cortical Transcranial Magnetic Stimulation

Neurological disorders require varying types and degrees of treatments depending on the symptoms and underlying causes of the disease. Patients suffering from medication-refractory symptoms often undergo further treatment in the form of brain stimulation, e.g. electroconvulsive therapy (ECT), transcranial direct current stimulation (tDCS), deep brain stimulation (DBS), or transcranial magnetic stimulation (TMS). These treatments are popular and have been shown to relieve various symptoms for patients with neurological conditions. However, the underlying effects of the stimulation, and subsequently the causes of symptom-relief, are not very well understood. In particular, TMS is a non-invasive brain stimulation therapy which uses time-varying magnetic fields to induce electric fields on the conductive parts of the brain. TMS has been FDA-approved for treatment of major depressive disorder for patients refractory to medication, as well as symptoms of migraine. Studies have shown that TMS has relieved severe depressive symptoms, although researchers believe that it is the deeper regions of the brain which are responsible for symptom relief. Many experts theorize that cortical stimulation such as TMS causes brain signals to propagate from the cortex to these deep brain regions, after which the synapses of the excited neurons are changed in such a way as to cause plasticity. It has also been widely observed that stimulation of the cortex causes signal firing at the deeper regions of the brain. However, the particular mechanisms behind TMS-caused signal propagation are unknown and understudied. Due to the non-invasive nature of TMS, this is an area in which investigation can be of significant benefit to the clinical community. We posit that a deeper understanding of this phenomenon may allow clinicians to explore the use of TMS for treatment of various other neurological symptoms and conditions. This thesis project seeks to investigate the various effects of TMS in the human brain, with respect to brain tissue stimulation as well as the cellular effects at the level of neurons. We present novel models of motor neuron circuitry and fiber tracts that will aid in the development of deep brain stimulation modalities using non-invasive treatment paradigms

Comparison of MRI Spectroscopy software packages performance and application on HCV-infected patients’ real data

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Laredo Gregorio, Carlos1H MRS is conceived as a pioneer methodology for brain metabolism inspection and health status appraisal. Post-processing interventions are required to obtain explicit metabolite quantification values from which to derive diagnosis. On the grounds of addressing and covering such operation, multiple software packages have been recently developed and launched leading to an amorphous assortment of spectroscopic image processing tools, with lack of standardization and regulation. The current study thereby intends to judge the coherence and consistency of compound estimation outputs in terms of result variability by intercorrelation and intracorrelation analyses between appointed programs, being LCModel, Osprey, TARQUIN, and spant toolbox. The examination is performed on a 83-subject SVS short-TE 3T SIEMENS PRESS spectroscopic acquisitions’ collection, including healthy controls and HCV-infected patients assisted with DAA treatment. The analytical core of the project assesses software performance through the creation of a Python script in order to automatically compute and display the results sought. The statistical tests providing enough information to draw substantial conclusions stem from extraction of coefficient of determination (R2 ), Pearson’s coefficient (r), and intraclass correlation coefficient (ICC) together with representation of boxplots, rainclouds, and scatter plots easing data visualization. A clinical implementation is also entailed on the same basis, whose purpose is to reveal actual DAA treatment effect on HCV-infected patients by means of metabolite concentration alteration and hypothetical restoration. Conclusions declare evident and alarming variability among MRS platforms compromising the rigor, sharpness and systematization demanded in this discipline since quantification results hold incoherences, although they do not seem to affect or oppose medical determinations jeopardizing patient’s health. However, it would be interesting to extend the analysis to a greater cohort of subjects to reinforce and get to more solid resolutions

Searching for the physical nature of intelligence in Neuromorphic Nanowire Networks

The brain’s unique information processing efficiency has inspired the development of neuromorphic, or brain-inspired, hardware in effort to reduce the power consumption of conventional Artificial Intelligence (AI). One example of a neuromorphic system is nanowire networks (NWNs). NWNs have been shown to produce conductance pathways similar to neuro-synaptic pathways in the brain, demonstrating nonlinear dynamics, as well as emergent behaviours such as memory and learning. Their synapse-like electro-chemical junctions are connected by a heterogenous neural network-like structure. This makes NWNs a unique system for realising hardware-based machine intelligence that is potentially more brain-like than existing implementations of AI. Much of the brain’s emergent properties are thought to arise from a unique structure-function relationship. The first part of the thesis establishes structural network characterisation methods in NWNs. Borrowing techniques from neuroscience, a toolkit is introduced for characterising network topology in NWNs. NWNs are found to display a ‘small-world’ structure with highly modular connections, like simple biological systems. Next, investigation of the structure-function link in NWNs occurs via implementation of machine learning benchmark tasks on varying network structures. Highly modular networks exhibit an ability to multitask, while integrated networks suffer from crosstalk interference. Finally, above findings are combined to develop and implement neuroscience-inspired learning methods and tasks in NWNs. Specifically, an adaptation of a cognitive task that tests working memory in humans is implemented. Working memory and memory consolidation are demonstrated and found to be attributable to a process similar to synaptic metaplasticity in the brain. The results of this thesis have created new research directions that warrant further exploration to test the universality of the physical nature of intelligence in inorganic systems beyond NWNs

New tools and specification languages for biophysically detailed neuronal network modelling

Increasingly detailed data are being gathered on the molecular, electrical and anatomical properties of neuronal systems both in vitro and in vivo. These range from the kinetic properties and distribution of ion channels, synaptic plasticity mechanisms, electrical activity in neurons, and detailed anatomical connectivity within neuronal microcircuits from connectomics data. Publications describing these experimental results often set them in the context of higher level network behaviour. Biophysically detailed computational modelling provides a framework for consolidating these data, for quantifying the assumptions about underlying biological mechanisms, and for ensuring consistency in the explanation of the phenomena across scales. Such multiscale biophysically detailed models are not currently in wide- spread use by the experimental neuroscience community however. Reasons for this include the relative inaccessibility of software for creating these models, the range of specialised scripting languages used by the available simulators, and the difficulty in creating and managing large scale network simulations. This thesis describes new solutions to facilitate the creation, simulation, analysis and reuse of biophysically detailed neuronal models. The graphical application neuroConstruct allows detailed cell and network models to be built in 3D, and run on multiple simulation platforms without detailed programming knowledge. NeuroML is a simulator independent language for describing models containing detailed neuronal morphologies, ion channels, synapses, and 3D network connectivity. New solutions have also been developed for creating and analysing network models at much closer to biological scale on high performance computing platforms. A number of detailed neocortical, cerebellar and hippocampal models have been converted for use with these tools. The tools and models I have developed have already started to be used for original scientific research. It is hoped that this work will lead to a more solid foundation for creating, validating, simulating and sharing ever more realistic models of neurons and networks

Evolvability signatures of generative encodings: beyond standard performance benchmarks

Evolutionary robotics is a promising approach to autonomously synthesize machines with abilities that resemble those of animals, but the field suffers from a lack of strong foundations. In particular, evolutionary systems are currently assessed solely by the fitness score their evolved artifacts can achieve for a specific task, whereas such fitness-based comparisons provide limited insights about how the same system would evaluate on different tasks, and its adaptive capabilities to respond to changes in fitness (e.g., from damages to the machine, or in new situations). To counter these limitations, we introduce the concept of "evolvability signatures", which picture the post-mutation statistical distribution of both behavior diversity (how different are the robot behaviors after a mutation?) and fitness values (how different is the fitness after a mutation?). We tested the relevance of this concept by evolving controllers for hexapod robot locomotion using five different genotype-to-phenotype mappings (direct encoding, generative encoding of open-loop and closed-loop central pattern generators, generative encoding of neural networks, and single-unit pattern generators (SUPG)). We observed a predictive relationship between the evolvability signature of each encoding and the number of generations required by hexapods to adapt from incurred damages. Our study also reveals that, across the five investigated encodings, the SUPG scheme achieved the best evolvability signature, and was always foremost in recovering an effective gait following robot damages. Overall, our evolvability signatures neatly complement existing task-performance benchmarks, and pave the way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary figures. Accepted at Information Sciences journal (in press). Supplemental videos are available online at, see http://goo.gl/uyY1R

29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong