Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models

During ongoing and Up state activity, cortical circuits manifest a set of dynamical features that are conserved across these states. The present work systematizes these phenomena by three notions: excitability, the ability to sustain activity without external input; balance, precise coordination of excitatory and inhibitory neuronal inputs; and stability, maintenance of activity at a steady level. Slice preparations exhibiting Up states demonstrate that balanced activity can be maintained by small local circuits. While computational models of cortical circuits have included different combinations of excitability, balance, and stability, they have done so without a systematic quantitative comparison with experimental data. Our study provides quantitative criteria for this purpose, by analyzing in-vitro and in-vivo neuronal activity and characterizing the dynamics on the neuronal and population levels. The criteria are defined with a tolerance that allows for differences between experiments, yet are sufficient to capture commonalities between persistently depolarized cortical network states and to help validate computational models of cortex. As test cases for the derived set of criteria, we analyze three widely used models of cortical circuits and find that each model possesses some of the experimentally observed features, but none satisfies all criteria simultaneously, showing that the criteria are able to identify weak spots in computational models. The criteria described here form a starting point for the systematic validation of cortical neuronal network models, which will help improve the reliability of future models, and render them better building blocks for larger models of the brain

Homogeneous and Narrow Bandwidth of Spike Initiation in Rat L1 Cortical Interneurons

The cortical layer 1 (L1) contains a population of GABAergic interneurons, considered a key component of information integration, processing, and relaying in neocortical networks. In fact, L1 interneurons combine top\u2013down information with feed-forward sensory inputs in layer 2/3 and 5 pyramidal cells (PCs), while filtering their incoming signals. Despite the importance of L1 for network emerging phenomena, little is known on the dynamics of the spike initiation and the encoding properties of its neurons. Using acute brain tissue slices from the rat neocortex, combined with the analysis of an existing database of model neurons, we investigated the dynamical transfer properties of these cells by sampling an entire population of known \u201celectrical classes\u201d and comparing experiments and model predictions. We found the bandwidth of spike initiation to be significantly narrower than in L2/3 and 5 PCs, with values below 100 cycle/s, but without significant heterogeneity in the cell response properties across distinct electrical types. The upper limit of the neuronal bandwidth was significantly correlated to the mean firing rate, as anticipated from theoretical studies but not reported for PCs. At high spectral frequencies, the magnitude of the neuronal response attenuated as a power-law, with an exponent significantly smaller than what was reported for pyramidal neurons and reminiscent of the dynamics of a \u201cleaky\u201d integrate-and-fire model of spike initiation. Finally, most of our in vitro results matched quantitatively the numerical simulations of the models as a further contribution to independently validate the models against novel experimental data

Moving from phenomenological to predictive modelling: Progress and pitfalls of modelling brain stimulation in-silico

Brain stimulation is an increasingly popular neuromodulatory tool used in both clinical and research settings; however, the effects of brain stimulation, particularly those of non-invasive stimulation, are variable. This variability can be partially explained by an incomplete mechanistic understanding, coupled with a combinatorial explosion of possible stimulation parameters. Computational models constitute a useful tool to explore the vast sea of stimulation parameters and characterise their effects on brain activity. Yet the utility of modelling stimulation in-silico relies on its biophysical relevance, which needs to account for the dynamics of large and diverse neural populations and how underlying networks shape those collective dynamics. The large number of parameters to consider when constructing a model is no less than those needed to consider when planning empirical studies. This piece is centred on the application of phenomenological and biophysical models in non-invasive brain stimulation. We first introduce common forms of brain stimulation and computational models, and provide typical construction choices made when building phenomenological and biophysical models. Through the lens of four case studies, we provide an account of the questions these models can address, commonalities, and limitations across studies. We conclude by proposing future directions to fully realise the potential of computational models of brain stimulation for the design of personalized, efficient, and effective stimulation strategies

Selective Adaptation in Networks of Heterogeneous Populations: Model, Simulation, and Experiment

Biological systems often change their responsiveness when subject to persistent stimulation, a phenomenon termed adaptation. In neural systems, this process is often selective, allowing the system to adapt to one stimulus while preserving its sensitivity to another. In some studies, it has been shown that adaptation to a frequent stimulus increases the system's sensitivity to rare stimuli. These phenomena were explained in previous work as a result of complex interactions between the various subpopulations of the network. A formal description and analysis of neuronal systems, however, is hindered by the network's heterogeneity and by the multitude of processes taking place at different time-scales. Viewing neural networks as populations of interacting elements, we develop a framework that facilitates a formal analysis of complex, structured, heterogeneous networks. The formulation developed is based on an analysis of the availability of activity dependent resources, and their effects on network responsiveness. This approach offers a simple mechanistic explanation for selective adaptation, and leads to several predictions that were corroborated in both computer simulations and in cultures of cortical neurons developing in vitro. The framework is sufficiently general to apply to different biological systems, and was demonstrated in two different cases

The role of the prefrontal cortex in the control of dual-task gait

Prefrontal cortex is frequently linked to dual-task gait performance; however, its precise role is unknown. The purpose of this thesis was to examine the role of prefrontal cortex in the control of dual-task gait. Using transcranial direct stimulation (tDCS) to alter prefrontal cortex activity, the influence of prefrontal cortex on dual-task gait performance and the corticospinal system was examined across four experiential studies using the guided activation framework of prefrontal cortex function (Miller and Cohen, 2001). The first study examined the role of cognitive task type and walking speed on stride time variability and trunk range of motion during dual-task walking. Results revealed the greatest dual-task cost on gait occurred when walking at a slow speed whilst simultaneously performing a serial subtraction task, compared to performance of a working memory task, providing a rationale for the use of this paradigm in later studies. The second study examined the effect of prefrontal tDCS on dual-task gait performance during both normal and slow walking. Anodal tDCS reduced the dualtask cost on both gait and cognitive task performance, and these effects were not dependent on walking speed. These results indicating that prefrontal tDCS may alter the allocation of cognitive control across tasks during dual-task gait, in accordance with established models of prefrontal cortex function. The third study examined the effect of prefrontal tDCS on corticospinal excitability and working memory performance. Results revealed that cathodal tDCS reduced corticospinal excitability. However, there was no effect of tDCS on working memory performance. Because prefrontal tDCS altered the activity in remote motor networks, these results indicated a possible mechanism by which prefrontal cortex exerts control over gait performance. In addition, because this study failed to replicate previous reports of working memory improvement following tDCS, these results also suggested a degree of inter-individual variability in response to tDCS. The final study examined the influence of walking modality and task difficulty on the effects of prefrontal tDCS on dual-task gait performance. tDCS altered the allocation of cognitive control during over-ground dual-task gait performance, and 3 these effects were mediated by task difficulty. In contrast to the second study, there was no effect of tDCS on treadmill dual-task gait. A secondary aim of the final study was to examine whether cognitive and walking task performance were coordinated. Results revealed that participants articulated answers during the initial swing phase of the gait cycle more frequently than other phases, indicating a degree of coordination between the performance of these tasks. Overall the finding of this thesis indicate that prefrontal cortex is involved in the allocation of cognitive control processes during dual-task walking, in accordance with the guided activation and flexible hub accounts of frontal cortex function (Miller and Cohen, 2001; Cole et al., 2013). These findings may have implications for the design and validation of strategies aimed at improving the cognitive control of gait

Neuroplasticity of Ipsilateral Cortical Motor Representations, Training Effects and Role in Stroke Recovery

This thesis examines the contribution of the ipsilateral hemisphere to motor control with the aim of evaluating the potential of the contralesional hemisphere to contribute to motor recovery after stroke. Predictive algorithms based on neurobiological principles emphasize integrity of the ipsilesional corticospinal tract as the strongest prognostic indicator of good motor recovery. In contrast, extensive lesions placing reliance on alternative contralesional ipsilateral motor pathways are associated with poor recovery. Within the predictive algorithms are elements of motor control that rely on contributions from ipsilateral motor pathways, suggesting that balanced, parallel contralesional contributions can be beneficial. Current therapeutic approaches have focussed on the maladaptive potential of the contralesional hemisphere and sought to inhibit its activity with neuromodulation. Using Transcranial Magnetic Stimulation I seek examples of beneficial plasticity in ipsilateral cortical motor representations of expert performers, who have accumulated vast amounts of deliberate practise training skilled bilateral activation of muscles habitually under ipsilateral control. I demonstrate that ipsilateral cortical motor representations reorganize in response to training to acquisition of skilled motor performance. Features of this reorganization are compatible with evidence suggesting ipsilateral importance in synergy representations, controlled through corticoreticulopropriospinal pathways. I demonstrate that ipsilateral plasticity can associate positively with motor recovery after stroke. Features of plastic change in ipsilateral cortical representations are shown in response to robotic training of chronic stroke patients. These findings have implications for the individualization of motor rehabilitation after stroke, and prompt reappraisal of the approach to therapeutic intervention in the chronic phase of stroke