Modelling the interactions that arise from neural dynamics in seizure genesis is challenging but important in the effort to improve the success of epilepsy surgery. Dynamical network models developed from physiological evidence offer insights into rapidly evolving brain networks in the epileptic seizure. A limitation of previous studies in this field is the dependence on invasive cortical recordings with constrained spatial sampling of brain regions that might be involved in seizure dynamics. Here, we propose virtual intracranial electroencephalography (ViEEG), which combines non-invasive ictal magnetoencephalographic imaging (MEG), dynamical network models and a virtual resection technique. In this proof-of-concept study, we show that ViEEG signals reconstructed from MEG alone preserve critical temporospatial characteristics for dynamical approaches to identify brain areas involved in seizure generation. We show the non-invasive ViEEG approach may have some advantage over intracranial electroencephalography (iEEG). Future work may be designed to test the potential of the virtual iEEG approach for use in surgical management of epilepsy.

Male zebra finches produce a song consisting of a canonical sequence of syllables, learned from a tutor and repeated throughout its adult life. Much of the neural circuitry responsible for this behavior is located in the cortical premotor region HVC (acronym is name). In a recent study from our laboratory, we found that partial bilateral ablation of the medial portion of HVC has effects on the song that are qualitatively different from those of bilateral ablation of the lateral portion. In this report we describe a neural network organization that can explain these data, and in so doing suggests key roles for other brain nuclei in the production of song. We also suggest that syllables and the gaps between them are each coded separately by neural chains within HVC, and that the timing mechanisms for syllables and gaps are distinct. The design principles underlying this model assign distinct roles for medial and lateral HVC circuitry that explain the data on medial and lateral ablations. In addition, despite the fact that the neural coding of song sequence is distributed among several brain nuclei in our model, it accounts for data showing that cooling of HVC stretches syllables uniformly and to a greater extent than gaps. Finally, the model made unanticipated predictions about details of the effects of medial and lateral HVC ablations that were then confirmed by reanalysis of these previously acquired behavioral data. Zebra finch song consists of a string of syllables repeated in a nearly invariant sequence. We propose a neural network organization that can explain recent data indicating that the medial and lateral portions of the premotor cortical nucleus HVC have different roles in zebra finch song production. Our model explains these data, as well as data on the effects on song of cooling HVC, and makes predictions that we test in the singing bird.

Senescent cells provide a good in vitro model to study ageing. However, cultures of ‘senescent’ cells consist of a mix of cell subtypes (proliferative, senescent, growth-arrested and apoptotic). Determining the proportion of senescent cells is crucial for studying ageing and developing new anti-degenerative therapies. Commonly used markers such as doubling population, senescence-associated β-galactosidase, Ki-67, γH2AX and TUNEL assays capture diverse and overlapping cellular populations and are not purely specific to senescence. A newly developed dynamical systems model follows the transition of an initial culture to senescence tracking population doubling, and the proportion of cells in proliferating, growth-arrested, apoptotic and senescent states. Our model provides a parsimonious description of transitions between these states accruing towards a predominantly senescent population. Using a genetic algorithm, these model parameters are well constrained by an in vitro human primary fibroblast dataset recording five markers at 16 time points. The computational model accurately fits to the data and translates these joint markers into the first complete description of the proportion of cells in different states over the lifetime. The high temporal resolution of the dataset demonstrates the efficacy of strategies for reconstructing the trajectory towards replicative senescence with a minimal number of experimental recordings.

Major surgery and critical illness produce a potentially life-threatening systemic inflammatory response. The hypothalamic–pituitary–adrenal (HPA) axis is one of the key physiological systems that counterbalances this systemic inflammation through changes in adrenocorticotrophic hormone (ACTH) and cortisol. These hormones normally exhibit highly correlated ultradian pulsatility with an amplitude modulated by circadian processes. However, these dynamics are disrupted by major surgery and critical illness. In this work, we characterize the inflammatory, ACTH and cortisol responses of patients undergoing cardiac surgery and show that the HPA axis response can be classified into one of three phenotypes: single-pulse, two-pulse and multiple-pulse dynamics. We develop a mathematical model of cortisol secretion and metabolism that predicts the physiological mechanisms responsible for these different phenotypes. We show that the effects of inflammatory mediators are important only in the single-pulse pattern in which normal pulsatility is lost—suggesting that this phenotype could be indicative of the greatest inflammatory response. Investigating whether and how these phenotypes are correlated with clinical outcomes will be critical to patient prognosis and designing interventions to improve recovery.

Male zebra finches produce a sequence-invariant set of syllables, separated by short inspiratory gaps. These songs are learned from an adult tutor and maintained throughout life, making them a tractable model system for learned, sequentially ordered behaviors, particularly speech production. Moreover, much is known about the cortical, thalamic, and brain stem areas involved in producing this behavior, with the premotor cortical nucleus HVC (proper name) being of primary importance. In a previous study, our group developed a behavioral neural network model for birdsong constrained by the structural connectivity of the song system, the signaling properties of individual neurons and circuits, and circuit-breaking behavioral studies. Here we describe a more computationally tractable model and use it to explain the behavioral effects of unilateral cooling and electrical stimulations of HVC on song production. The model demonstrates that interhemispheric switching of song control is sufficient to explain these results, consistent with the hypotheses proposed when the experiments were initially conducted. Finally, we use the model to make testable predictions that can be used to validate the model framework and explain the effects of other perturbations of the song system, such as unilateral ablations of the primary input and output nuclei of HVC. NEW & NOTEWORTHY In this report, we propose a two-hemisphere neural network model for the bilaterally symmetrical song system underlying birdsong in the male zebra finch. This model captures the behavioral effects of unilateral cooling and electrical stimulations of the premotor cortical nucleus HVC during song production, supporting the hypothesis of interhemispheric switching of song control. We use the model to make testable predictions regarding the behavioral effects of other unilateral perturbations to the song system.

Major surgery and critical illness produce a potentially life threatening systemic inflammatory response. The hypothalamic-pituitary-adrenal (HPA) axis is one of the key physiological systems that counterbalances this systemic inflammation through changes in adrenocorticotrophic hormone (ACTH) and cortisol. These hormones normally exhibit highly correlated ultradian pulsatility with an amplitude modulated by circadian processes. However, these dynamics are disrupted by major surgery and critical illness. In this work, we characterise the inflammatory, ACTH and cortisol responses of patients undergoing cardiac surgery and show that the HPA axis response can be classified into one of three phenotypes: single-pulse, two-pulses and multiple-pulses dynamics. We develop a mathematical model of cortisol secretion and metabolism that predicts the physiological mechanisms responsible for these different phenotypes. We show that the effects of inflammatory mediators are important only in the single-pulse pattern in which normal pulsatility is lost – suggesting that this phenotype could be indicative of the greatest inflammatory response. Investigating whether and how these phenotypes are correlated with clinical outcomes will be critical to patient prognosis and designing interventions to improve recovery.

Epilepsy is one of the most common neurological disorders in children. Diagnosing epilepsy in children can be very challenging, especially as it often coexists with neurodevelopmental conditions like autism and ADHD. Functional brain networks obtained from neuroimaging and electrophysiological data in wakefulness and sleep have been shown to contain signatures of neurological disorders, and can potentially support the diagnosis and management of co-occurring neurodevelopmental conditions. In this work, we use electroencephalography (EEG) recordings from children, in restful wakefulness and sleep, to extract functional connectivity networks in different frequency bands. We explore the relationship of these networks with epilepsy diagnosis and with measures of neurodevelopmental traits, obtained from questionnaires used as screening tools for autism and ADHD. We explore differences in network markers between children with and without epilepsy in wake and sleep, and quantify the correlation between such markers and measures of neurodevelopmental traits. Our findings highlight the importance of considering the interplay between epilepsy and neurodevelopmental traits when exploring network markers of epilepsy.

Modelling the interactions that arise from neural dynamics in seizure genesis is challenging but important in the effort to improve the success of epilepsy surgery. Dynamical network models developed from physiological evidence offer insights into rapidly evolving brain networks in the epileptic seizure. A major limitation of previous studies in this field is the dependence on invasive cortical recordings with constrained spatial sampling of brain regions that might be involved in seizure dynamics. Here, we propose a novel approach, virtual intracranial electroencephalography (ViEEG), that combines non-invasive ictal magnetoencephalographic imaging (MEG), dynamical network models and a virtual resection technique. In this proof-of-concept study, we show that ViEEG signals reconstructed from MEG alone preserve critical temporospatial characteristics for dynamical approaches to identify brain areas involved in seizure generation. Our findings demonstrate the advantages of non-invasive ViEEG over the current presurgical ‘gold-standard’ – intracranial electroencephalography (iEEG). Our approach promises to optimise the surgical strategy for patients with complex refractory focal epilepsy.